feat: Initial ONNX export of LiquidAI/LFM2.5-230M

- Full set of variants: FP32, FP16, Q4 (GatherBlockQuantized + MatMulNBits), Q4F32, Q8
- Enhanced README with standalone Python + WebGPU inference examples (inspired by LFM2.5-350M-ONNX)
- Includes tokenizer, chat template, configs
- Verified with onnxruntime generation loop

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+onnx/model.onnx_data filter=lfs diff=lfs merge=lfs -text
+onnx/model_fp16.onnx_data filter=lfs diff=lfs merge=lfs -text
+onnx/model_q4.onnx_data filter=lfs diff=lfs merge=lfs -text
+onnx/model_q4f32.onnx_data filter=lfs diff=lfs merge=lfs -text
+onnx/model_q8.onnx_data filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,71 @@
+LFM Open License v1.0
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+END OF TERMS AND CONDITIONS

README.md

@@ -0,0 +1,379 @@
+---
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+  - en
+  - ar
+  - zh
+  - fr
+  - de
+  - ja
+  - ko
+  - es
+  - pt
+  - it
+pipeline_tag: text-generation
+tags:
+  - liquid
+  - edge
+  - lfm2.5
+  - onnx
+  - onnxruntime
+  - webgpu
+  - conversational
+base_model:
+  - LiquidAI/LFM2.5-230M
+---
+
+<div align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI" 
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
+    <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • 
+    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
+    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • 
+    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+  </div>
+</div>
+<br>
+
+# LFM2.5-230M-ONNX
+
+ONNX export of [LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) for cross-platform inference.
+
+LFM2.5 is a hybrid architecture combining multiplicative gates and short convolutions, optimized for edge deployment with fast inference on CPU, GPU, and NPU hardware.
+
+## Recommended Variants
+
+| Variant | Size    | Description |
+|---------|---------|-------------|
+| FP16    | ~455 MB | All weights in FP16 |
+| Q4      | ~200 MB | INT4 embedding (GatherBlockQuantized), INT4 lm_head (MatMulNBits, shared), INT4 MatMul weights |
+| Q4F32   | ~390 MB | INT4 MatMul weights, FP32 embedding and norms |
+| Q8      | ~470 MB | INT8 MatMul weights, FP32 embedding and norms |
+
+Q4 uses GatherBlockQuantized for the token embedding and MatMulNBits for the lm_head,
+reusing the same quantized weights and scales. All other linear layers are quantized to
+INT4 via post-export MatMulNBitsQuantizer. Block size is 32.
+
+Q4F32 keeps the embedding as a FP32 Gather and the lm_head as FP32 Transpose + MatMul.
+Only the internal linear layers (attention projections, conv projections, MLP) are
+quantized to INT4 via post-export MatMulNBitsQuantizer.
+
+Q8 is the same structure as Q4F32 but with INT8 weights (asymmetric quantization).
+
+## Generation Parameters
+
+| Parameter           | Value |
+|---------------------|-------|
+| `temperature`       | 0.1   |
+| `top_k`             | 50    |
+| `repetition_penalty`| 1.05  |
+
+## Model Files
+
+```
+onnx/
+├── model.onnx              # FP32
+├── model_fp16.onnx         # FP16
+├── model_q4.onnx           # Q4
+├── model_q4f32.onnx        # Q4F32
+└── model_q8.onnx           # Q8
+```
+
+## Python
+
+### Installation
+
+```bash
+pip install onnxruntime transformers numpy huggingface_hub
+# or with GPU support:
+pip install onnxruntime-gpu transformers numpy huggingface_hub
+```
+
+### Inference
+
+```python
+import numpy as np
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+from transformers import AutoTokenizer
+
+# Download model
+model_id = "LiquidAI/LFM2.5-230M-ONNX"
+model_path = hf_hub_download(model_id, "onnx/model_q4.onnx")
+data_path = hf_hub_download(model_id, "onnx/model_q4.onnx_data")
+
+# Load model and tokenizer
+session = ort.InferenceSession(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+# Sampling parameters
+TEMPERATURE = 0.1
+TOP_K = 50
+REPETITION_PENALTY = 1.05
+
+# Prepare chat input
+messages = [{"role": "user", "content": "What is the capital of France?"}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+input_ids = np.array([tokenizer.encode(prompt, add_special_tokens=False)], dtype=np.int64)
+
+# Initialize KV cache
+ONNX_DTYPE = {"tensor(float)": np.float32, "tensor(float16)": np.float16, "tensor(int64)": np.int64}
+cache = {}
+for inp in session.get_inputs():
+    if inp.name in {"input_ids", "attention_mask", "position_ids"}:
+        continue
+    shape = [d if isinstance(d, int) else 1 for d in inp.shape]
+    for i, d in enumerate(inp.shape):
+        if isinstance(d, str) and "sequence" in d.lower():
+            shape[i] = 0
+    cache[inp.name] = np.zeros(shape, dtype=ONNX_DTYPE.get(inp.type, np.float32))
+
+# Check if model uses position_ids
+input_names = {inp.name for inp in session.get_inputs()}
+use_position_ids = "position_ids" in input_names
+
+
+def sample_token(logits, generated_tokens):
+    """Sample next token with temperature, top-k, and repetition penalty."""
+    # Apply repetition penalty
+    for token_id in set(generated_tokens):
+        if logits[token_id] > 0:
+            logits[token_id] /= REPETITION_PENALTY
+        else:
+            logits[token_id] *= REPETITION_PENALTY
+
+    # Apply temperature
+    logits = logits / TEMPERATURE
+
+    # Top-k filtering
+    top_k_indices = np.argpartition(logits, -TOP_K)[-TOP_K:]
+    top_k_logits = logits[top_k_indices]
+
+    # Softmax over top-k
+    top_k_logits -= np.max(top_k_logits)
+    probs = np.exp(top_k_logits) / np.sum(np.exp(top_k_logits))
+
+    # Sample
+    chosen = np.random.choice(len(top_k_indices), p=probs)
+    return int(top_k_indices[chosen])
+
+
+# Generate tokens
+seq_len = input_ids.shape[1]
+generated_tokens = []
+
+for step in range(512):  # max tokens
+    if step == 0:
+        ids = input_ids
+        pos = np.arange(seq_len, dtype=np.int64).reshape(1, -1)
+    else:
+        ids = np.array([[generated_tokens[-1]]], dtype=np.int64)
+        pos = np.array([[seq_len + len(generated_tokens) - 1]], dtype=np.int64)
+
+    attn_mask = np.ones((1, seq_len + len(generated_tokens)), dtype=np.int64)
+    feed = {"input_ids": ids, "attention_mask": attn_mask, **cache}
+    if use_position_ids:
+        feed["position_ids"] = pos
+
+    outputs = session.run(None, feed)
+    logits = outputs[0][0, -1].copy()
+    next_token = sample_token(logits, generated_tokens)
+    generated_tokens.append(next_token)
+
+    # Update cache
+    for i, out in enumerate(session.get_outputs()[1:], 1):
+        name = out.name.replace("present_conv", "past_conv").replace("present.", "past_key_values.")
+        if name in cache:
+            cache[name] = outputs[i]
+
+    if next_token == tokenizer.eos_token_id:
+        break
+
+print(tokenizer.decode(generated_tokens, skip_special_tokens=True))
+```
+
+## WebGPU (Browser)
+
+### Installation
+
+```bash
+npm install onnxruntime-web @huggingface/transformers
+```
+
+### Enable WebGPU
+
+WebGPU is required for browser inference. To enable:
+
+1. **Chrome/Edge**: Navigate to `chrome://flags/#enable-unsafe-webgpu`, enable, and restart
+2. **Verify**: Check `chrome://gpu` for "WebGPU" status
+3. **Test**: Run `navigator.gpu.requestAdapter()` in DevTools console
+
+### Inference
+
+```javascript
+import * as ort from "onnxruntime-web/webgpu";
+import { AutoTokenizer } from "@huggingface/transformers";
+
+// Check WebGPU availability
+if (!navigator.gpu) {
+  throw new Error("WebGPU not available. Enable at chrome://flags/#enable-unsafe-webgpu");
+}
+const adapter = await navigator.gpu.requestAdapter();
+if (!adapter) {
+  throw new Error("WebGPU adapter not found. Check chrome://gpu for status.");
+}
+
+ort.env.wasm.numThreads = 1;
+
+const modelId = "LiquidAI/LFM2.5-230M-ONNX";
+const modelBase = `https://huggingface.co/${modelId}/resolve/main`;
+
+// Load tokenizer
+const tokenizer = await AutoTokenizer.from_pretrained(modelId);
+
+// Load ONNX session with external data
+const onnxPath = `${modelBase}/onnx/model_q4.onnx`;
+const dataPath = `${modelBase}/onnx/model_q4.onnx_data`;
+const session = await ort.InferenceSession.create(onnxPath, {
+  executionProviders: ["webgpu"],
+  externalData: [{ path: "model_q4.onnx_data", data: dataPath }],
+});
+
+// Sampling parameters
+const TEMPERATURE = 0.1;
+const TOP_K = 50;
+const REPETITION_PENALTY = 1.05;
+
+// Model config (from config.json)
+const hiddenSize = 1024;
+const numKVHeads = 8;
+const headDim = 64;
+
+// Initialize KV cache
+function initCache() {
+  const cache = {};
+  for (const name of session.inputNames) {
+    if (name.startsWith("past_conv")) {
+      cache[name] = new ort.Tensor("float32", new Float32Array(hiddenSize * 3), [1, hiddenSize, 3]);
+    } else if (name.startsWith("past_key_values")) {
+      cache[name] = new ort.Tensor("float32", new Float32Array(0), [1, numKVHeads, 0, headDim]);
+    }
+  }
+  return cache;
+}
+
+// Update cache from outputs
+function updateCache(cache, outputs) {
+  for (const [name, tensor] of Object.entries(outputs)) {
+    if (name.startsWith("present_conv")) {
+      cache[name.replace("present_conv", "past_conv")] = tensor;
+    } else if (name.startsWith("present.")) {
+      cache[name.replace("present.", "past_key_values.")] = tensor;
+    }
+  }
+}
+
+// Sample next token with temperature, top-k, and repetition penalty
+function sampleToken(logitsData, vocabSize, generatedTokens) {
+  const logits = new Float32Array(logitsData);
+
+  // Apply repetition penalty
+  const seen = new Set(generatedTokens);
+  for (const tokenId of seen) {
+    if (logits[tokenId] > 0) {
+      logits[tokenId] /= REPETITION_PENALTY;
+    } else {
+      logits[tokenId] *= REPETITION_PENALTY;
+    }
+  }
+
+  // Apply temperature
+  for (let i = 0; i < vocabSize; i++) {
+    logits[i] /= TEMPERATURE;
+  }
+
+  // Top-k: find top K indices
+  const indexed = Array.from(logits.slice(0, vocabSize), (v, i) => [v, i]);
+  indexed.sort((a, b) => b[0] - a[0]);
+  const topK = indexed.slice(0, TOP_K);
+
+  // Softmax over top-k
+  const maxLogit = topK[0][0];
+  const exps = topK.map(([v, i]) => [Math.exp(v - maxLogit), i]);
+  const sumExp = exps.reduce((s, [e]) => s + e, 0);
+  const probs = exps.map(([e, i]) => [e / sumExp, i]);
+
+  // Sample from distribution
+  let r = Math.random();
+  for (const [p, i] of probs) {
+    r -= p;
+    if (r <= 0) return i;
+  }
+  return probs[probs.length - 1][1];
+}
+
+// Build prompt and tokenize
+const messages = [{ role: "user", content: "What is the capital of France?" }];
+const prompt = tokenizer.apply_chat_template(messages, { add_generation_prompt: true, tokenize: false });
+const inputIds = tokenizer.encode(prompt);
+
+// Generation loop
+const cache = initCache();
+const eosTokenId = tokenizer.eos_token_id;
+const generatedTokens = [];
+let curLen = inputIds.length;
+let ids = inputIds;
+
+for (let step = 0; step < 512; step++) {
+  const inputIdsTensor = new ort.Tensor("int64", new BigInt64Array(ids.map(BigInt)), [1, ids.length]);
+  const attentionMask = new ort.Tensor("int64", new BigInt64Array(curLen).fill(1n), [1, curLen]);
+
+  const outputs = await session.run({ input_ids: inputIdsTensor, attention_mask: attentionMask, ...cache });
+
+  const logits = outputs.logits;
+  const vocabSize = logits.dims[2];
+  const lastLogits = logits.data.slice((logits.dims[1] - 1) * vocabSize, logits.dims[1] * vocabSize);
+  const nextToken = sampleToken(lastLogits, vocabSize, generatedTokens);
+
+  generatedTokens.push(nextToken);
+  if (nextToken === eosTokenId) break;
+
+  updateCache(cache, outputs);
+  ids = [nextToken];
+  curLen++;
+}
+
+console.log(tokenizer.decode(generatedTokens, { skip_special_tokens: true }));
+```
+
+### WebGPU Notes
+
+* Models use external data files (`.onnx_data`) that are loaded automatically
+* int64 tensors require `BigInt64Array`
+
+## Building from Source
+
+This ONNX package was created using the official [Liquid4All/onnx-export](https://github.com/Liquid4All/onnx-export) tool (the reference exporter for all LFM2/LFM2.5 ONNX checkpoints).
+
+```bash
+git clone https://github.com/Liquid4All/onnx-export.git
+cd onnx-export
+uv sync
+
+# Export all precisions (fp16, q4, q4f32, q8) + FP32 base
+uv run lfm2-export LiquidAI/LFM2.5-230M --precision
+```
+
+The output lands in `exports/LFM2.5-230M-ONNX/`. Copy its contents (or the `onnx/` subdir + metadata) to your target repo root for publishing to the Hugging Face Hub.
+
+The builder manually constructs the ONNX graph (using `onnx.helper`) for maximum compatibility with ONNX Runtime WebGPU, Transformers.js, and Microsoft custom ops (SimplifiedLayerNormalization, RotaryEmbedding, GroupQueryAttention, GatherBlockQuantized, MatMulNBits, etc.). Standard `torch.onnx.export` is not used.
+
+## License
+
+This model is released under the [LFM 1.0 License](LICENSE).

chat_template.jinja

@@ -0,0 +1,115 @@
+{{- bos_token -}}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+
+{%- macro format_arg_value(arg_value) -%}
+    {%- if arg_value is string -%}
+        {{- "'" + arg_value + "'" -}}
+    {%- elif arg_value is mapping -%}
+        {{- arg_value | tojson -}}
+    {%- else -%}
+        {{- arg_value | string -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro parse_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- else -%}
+        {%- set _ns = namespace(result="") -%}
+        {%- for item in content -%}
+            {%- if item["type"] == "image" -%}
+                {%- set _ns.result = _ns.result + "<image>" -%}
+            {%- elif item["type"] == "text" -%}
+                {%- set _ns.result = _ns.result + item["text"] -%}
+            {%- else -%}
+                {%- set _ns.result = _ns.result + item | tojson -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {{- _ns.result -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_calls(tool_calls) -%}
+    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}
+    {%- for tool_call in tool_calls -%}
+        {%- set func_name = tool_call["function"]["name"] -%}
+        {%- set func_args = tool_call["function"]["arguments"] -%}
+        {%- set args_ns = namespace(arg_strings=[]) -%}
+        {%- for arg_name, arg_value in func_args.items() -%}
+            {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
+        {%- endfor -%}
+        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
+    {%- endfor -%}
+    {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(system_prompt="", last_user_index=-1) -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- if messages[0].get("content") -%}
+        {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message.role + "\n" -}}
+    {%- if message.role == "assistant" -%}
+        {%- generation -%}
+        {%- if message.thinking is defined and (preserve_thinking or loop.index0 > ns.last_user_index) -%}
+            {{- "<think>" + message.thinking + "</think>" -}}
+        {%- endif -%}
+        {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%}
+        {%- set _has_cfm = false -%}
+        {%- if message.content is defined -%}
+            {%- set content = parse_content(message.content) -%}
+            {%- if not (preserve_thinking or loop.index0 > ns.last_user_index) -%}
+                {%- if "</think>" in content -%}
+                    {%- set content = content.split("</think>")[-1] | trim -%}
+                {%- endif -%}
+            {%- endif -%}
+            {%- if message.tool_calls is defined and content.endswith(_cfm_tag) -%}
+                {%- set _has_cfm = true -%}
+                {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}
+                {{- content[:_trunc_len] -}}
+            {%- else -%}
+                {{- content -}}
+            {%- endif -%}
+        {%- endif -%}
+        {%- if message.tool_calls is defined -%}
+            {{- render_tool_calls(message.tool_calls) -}}
+        {%- endif -%}
+        {%- if _has_cfm -%}
+            {{- _cfm_tag -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+        {%- endgeneration -%}
+    {%- else %}
+        {%- if message.get("content") -%}
+            {{- parse_content(message["content"]) -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+    {%- endif %}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

@@ -0,0 +1,71 @@
+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block__name_mlp": "parallel_mlp_merged",
+  "block_auto_adjust_ff_dim": false,
+  "block_dim": 1024,
+  "block_ff_dim": 2560,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_ffn_te_autocast": false,
+  "block_ffn_use_quantized_params": false,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_sequence_parallel_norm_across_tp": false,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 1,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "conv_dim": 1024,
+  "conv_use_xavier_init": true,
+  "dtype": "bfloat16",
+  "eos_token_id": 7,
+  "ffn_te_autocast": false,
+  "ffn_use_quantized_params": false,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_heads": 16,
+  "num_hidden_layers": 14,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sequence_parallel_norm_across_tp": false,
+  "tie_embedding": true,
+  "transformers_version": "5.0.0.dev0",
+  "use_cache": true,
+  "use_pos_enc": true,
+  "vocab_size": 65536,
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp32": "float32"
+    },
+    "use_external_data_format": true
+  }
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 7,
+  "pad_token_id": 0,
+  "transformers_version": "4.54.0"
+}

onnx/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f85055f79e7e0e5d4f5c1de63cc61d587d5ba080165a7b8eb3f24c1835b755b
+size 128784

onnx/model.onnx_data

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+version https://git-lfs.github.com/spec/v1
+oid sha256:80116563ab2f55afdaeb0d7561f0f5c2a509f5b0770a290b4f595aa4f1c32147
+size 951537664

onnx/model_fp16.onnx

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+oid sha256:e5f6de5c62a5f2926864c02d1cb5af7ae3b9a9a91514bd685fad4ac14d04dbee
+size 133966

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+size 475768832

onnx/model_q4.onnx

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+oid sha256:82a442c44d3d143432edff57984a4e7e8da65179d3a960a170d294aed8c5dd8d
+size 154010

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+oid sha256:b51a4580a88a2cd0486032cfdaf8694b09359abca5a97df8deb0a314ea6e5b34
+size 211111936

onnx/model_q4f32.onnx

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+oid sha256:532477d96f2bda9f7eef984799f3c049259970bf4533c528e71fca85da283528
+size 153902

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+oid sha256:ecc930d0f6e2f6fc2326730b82fd25cd1542da078af0d7ee2319edb39b9be939
+size 403001344

onnx/model_q8.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4cfeb296227815f954a1b37c7d8a65c3e8bc264ecf463fc655963ab5bdb1e074
+size 167040

onnx/model_q8.onnx_data

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65b7e60d582227df4a0ab97cc9cd18e0a73dad718200f172fac7f080986b4ac7
+size 489345024

tokenizer_config.json

@@ -0,0 +1,20 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "is_local": true,
+  "legacy": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "use_default_system_prompt": false,
+  "use_fast": true,
+  "chat_template": "{{- bos_token -}}\n{%- set preserve_thinking = preserve_thinking | default(false) -%}\n\n{%- macro format_arg_value(arg_value) -%}\n    {%- if arg_value is string -%}\n        {{- \"'\" + arg_value + \"'\" -}}\n    {%- elif arg_value is mapping -%}\n        {{- arg_value | tojson -}}\n    {%- else -%}\n        {{- arg_value | string -}}\n    {%- endif -%}\n{%- endmacro -%}\n\n{%- macro parse_content(content) -%}\n    {%- if content is string -%}\n        {{- content -}}\n    {%- else -%}\n        {%- set _ns = namespace(result=\"\") -%}\n        {%- for item in content -%}\n            {%- if item[\"type\"] == \"image\" -%}\n                {%- set _ns.result = _ns.result + \"<image>\" -%}\n            {%- elif item[\"type\"] == \"text\" -%}\n                {%- set _ns.result = _ns.result + item[\"text\"] -%}\n            {%- else -%}\n                {%- set _ns.result = _ns.result + item | tojson -%}\n            {%- endif -%}\n        {%- endfor -%}\n        {{- _ns.result -}}\n    {%- endif -%}\n{%- endmacro -%}\n\n{%- macro render_tool_calls(tool_calls) -%}\n    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}\n    {%- for tool_call in tool_calls -%}\n        {%- set func_name = tool_call[\"function\"][\"name\"] -%}\n        {%- set func_args = tool_call[\"function\"][\"arguments\"] -%}\n        {%- set args_ns = namespace(arg_strings=[]) -%}\n        {%- for arg_name, arg_value in func_args.items() -%}\n            {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + \"=\" + format_arg_value(arg_value)] -%}\n        {%- endfor -%}\n        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + \"(\" + (args_ns.arg_strings | join(\", \")) + \")\"] -%}\n    {%- endfor -%}\n    {{- \"<|tool_call_start|>[\" + (tool_calls_ns.tool_calls | join(\", \")) + \"]<|tool_call_end|>\" -}}\n{%- endmacro -%}\n\n{%- set ns = namespace(system_prompt=\"\", last_user_index=-1) -%}\n{%- if messages[0][\"role\"] == \"system\" -%}\n    {%- if messages[0].get(\"content\") -%}\n        {%- set ns.system_prompt = parse_content(messages[0][\"content\"]) -%}\n    {%- endif -%}\n    {%- set messages = messages[1:] -%}\n{%- endif -%}\n{%- if tools -%}\n    {%- set ns.system_prompt = ns.system_prompt + (\"\\n\" if ns.system_prompt else \"\") + \"List of tools: [\" -%}\n    {%- for tool in tools -%}\n        {%- if tool is not string -%}\n            {%- set tool = tool | tojson -%}\n        {%- endif -%}\n        {%- set ns.system_prompt = ns.system_prompt + tool -%}\n        {%- if not loop.last -%}\n            {%- set ns.system_prompt = ns.system_prompt + \", \" -%}\n        {%- endif -%}\n    {%- endfor -%}\n    {%- set ns.system_prompt = ns.system_prompt + \"]\" -%}\n{%- endif -%}\n{%- if ns.system_prompt -%}\n    {{- \"<|im_start|>system\\n\" + ns.system_prompt + \"<|im_end|>\\n\" -}}\n{%- endif -%}\n{%- for message in messages -%}\n    {%- if message[\"role\"] == \"user\" -%}\n        {%- set ns.last_user_index = loop.index0 -%}\n    {%- endif -%}\n{%- endfor -%}\n{%- for message in messages -%}\n    {{- \"<|im_start|>\" + message.role + \"\\n\" -}}\n    {%- if message.role == \"assistant\" -%}\n        {%- generation -%}\n        {%- if message.thinking is defined and (preserve_thinking or loop.index0 > ns.last_user_index) -%}\n            {{- \"<think>\" + message.thinking + \"</think>\" -}}\n        {%- endif -%}\n        {%- set _cfm_tag = \"CONTINUE_FINAL_MESSAGE_TAG \" -%}\n        {%- set _has_cfm = false -%}\n        {%- if message.content is defined -%}\n            {%- set content = parse_content(message.content) -%}\n            {%- if not (preserve_thinking or loop.index0 > ns.last_user_index) -%}\n                {%- if \"</think>\" in content -%}\n                    {%- set content = content.split(\"</think>\")[-1] | trim -%}\n                {%- endif -%}\n            {%- endif -%}\n            {%- if message.tool_calls is defined and content.endswith(_cfm_tag) -%}\n                {%- set _has_cfm = true -%}\n                {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}\n                {{- content[:_trunc_len] -}}\n            {%- else -%}\n                {{- content -}}\n            {%- endif -%}\n        {%- endif -%}\n        {%- if message.tool_calls is defined -%}\n            {{- render_tool_calls(message.tool_calls) -}}\n        {%- endif -%}\n        {%- if _has_cfm -%}\n            {{- _cfm_tag -}}\n        {%- endif -%}\n        {{- \"<|im_end|>\\n\" -}}\n        {%- endgeneration -%}\n    {%- else %}\n        {%- if message.get(\"content\") -%}\n            {{- parse_content(message[\"content\"]) -}}\n        {%- endif -%}\n        {{- \"<|im_end|>\\n\" -}}\n    {%- endif %}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{- \"<|im_start|>assistant\\n\" -}}\n{%- endif -%}"
+}