Push model

Co-authored-by: ykhrustalev <ykhrustalev@users.noreply.huggingface.co>

.gitattributes

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+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+onnx/model_q8.onnx_data_14 filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+LICENSE TEXT
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README.md

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+---
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- liquid
+- edge
+- lfm2
+- lfm2.5
+- moe
+- mixture-of-experts
+- onnx
+- onnxruntime
+base_model:
+- LiquidAI/LFM2.5-8B-A1B
+---
+
+<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>
+
+# LFM2.5-8B-A1B-ONNX
+
+ONNX export of [LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) for cross-platform inference.
+
+LFM2.5-8B-A1B is a Mixture of Experts model with 8B total parameters and about 1B active parameters per token. It uses 32 experts with 4 experts activated per token, combining the efficiency of sparse models with the quality of larger dense models.
+
+## Recommended Variants
+
+| Precision | Size | Use Case |
+|-----------|------|----------|
+| Q4F16 | ~4.7GB | Recommended (Q4 MoE + FP16 dense) |
+| FP16 | ~15.8GB | Higher quality |
+| Q4 | ~5.2GB | Smallest size |
+| Q8 | ~30.4GB | Highest-fidelity quantized variant |
+
+Note: This model is too large for WebGPU browser inference.
+
+## Validation
+
+This export was validated against the local PyTorch reference for `LiquidAI/LFM2.5-8B-A1B`.
+
+- FP32 padded-batch parity passed for both left and right padding, with cosine similarity `1.0000` and top-5 overlap `5/5` at the last valid token for each row.
+- Q4 decoder and coherence checks passed the repository thresholds. Average coherence similarity: `0.7144`.
+- Q4F16 was runtime-validated on `CPUExecutionProvider` and matched the same decoder/coherence thresholds as Q4. Average coherence similarity: `0.7145`.
+- Q8 decoder and coherence checks passed, and stayed very close to the PyTorch reference. Average coherence similarity: `0.9975`.
+
+## Model Files
+
+```
+onnx/
+├── model.onnx              # FP32 model graph
+├── model.onnx_data*        # FP32 weights
+├── model_fp16.onnx         # FP16 model graph
+├── model_fp16.onnx_data*   # FP16 weights
+├── model_q4.onnx           # Q4 model graph
+├── model_q4.onnx_data*     # Q4 weights
+├── model_q4f16.onnx        # Q4 MoE experts + FP16 dense (recommended)
+├── model_q4f16.onnx_data*  # Q4F16 weights
+├── model_q8.onnx           # Q8 model graph
+└── model_q8.onnx_data*     # Q8 weights
+
+* Large models split weights across multiple files:
+  model.onnx_data, model.onnx_data_1, model.onnx_data_2, etc.
+  All data files must be in the same directory as the .onnx file.
+```
+
+## Python
+
+### Installation
+
+```bash
+pip install onnxruntime transformers numpy huggingface_hub
+# or with GPU support:
+pip install onnxruntime-gpu transformers numpy huggingface_hub
+```
+
+### Inference
+
+```python
+from huggingface_hub import snapshot_download
+from transformers import AutoConfig, AutoTokenizer
+import numpy as np
+import onnxruntime
+
+# 1. Load config, tokenizer, and model
+model_id = "LiquidAI/LFM2.5-8B-A1B-ONNX"
+config = AutoConfig.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+eos_token_id = config.eos_token_id
+
+filename = "model_q4f16.onnx"  # Options: model.onnx, model_fp16.onnx, model_q4.onnx, model_q4f16.onnx, model_q8.onnx
+model_path = snapshot_download(repo_id=model_id, allow_patterns=f"onnx/{filename}*")
+session = onnxruntime.InferenceSession(f"{model_path}/onnx/{filename}")
+input_names = {inp.name for inp in session.get_inputs()}
+
+# 2. Prepare inputs
+prompt = "What is C. elegans?"
+messages = [{"role": "user", "content": prompt}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="np",
+)
+input_ids = inputs["input_ids"]
+attention_mask = inputs["attention_mask"]
+batch_size = input_ids.shape[0]
+
+past_cache_values = {}
+for inp in session.get_inputs():
+    name = inp.name
+    shape = inp.shape
+    dtype = np.float32 if inp.type == "tensor(float)" else np.float16
+    if name.startswith("past_key_values"):
+        past_cache_values[name] = np.zeros([batch_size, shape[1], 0, shape[3]], dtype=dtype)
+    elif name.startswith("past_conv"):
+        past_cache_values[name] = np.zeros([batch_size, shape[1], shape[2]], dtype=dtype)
+
+position_ids = np.arange(input_ids.shape[1], dtype=np.int64).reshape(1, -1)
+
+# 3. Generation loop
+max_new_tokens = 256
+generated_tokens = np.array([[]], dtype=np.int64)
+cur_len = input_ids.shape[1]
+for i in range(max_new_tokens):
+    if i == 0:
+        ids = input_ids
+        pos = position_ids
+    else:
+        ids = generated_tokens[:, -1:]
+        pos = np.array([[cur_len - 1]], dtype=np.int64)
+
+    feed = {
+        "input_ids": ids,
+        "attention_mask": attention_mask,
+        **past_cache_values,
+    }
+    if "position_ids" in input_names:
+        feed["position_ids"] = pos
+
+    outputs = session.run(None, feed)
+    logits = outputs[0]
+    next_token = logits[:, -1].argmax(-1, keepdims=True)
+
+    generated_tokens = (
+        next_token if generated_tokens.shape[1] == 0
+        else np.concatenate([generated_tokens, next_token], axis=-1)
+    )
+    attention_mask = np.concatenate(
+        [attention_mask, np.ones_like(next_token, dtype=np.int64)],
+        axis=-1,
+    )
+
+    output_names = [out.name for out in session.get_outputs()]
+    cache_outputs = {
+        name: value
+        for name, value in zip(output_names[1:], outputs[1:])
+    }
+    for key in past_cache_values:
+        present_key = key.replace("past_key_values", "present").replace("past_conv", "present_conv")
+        past_cache_values[key] = cache_outputs[present_key]
+
+    cur_len += 1
+    if np.isin(next_token, eos_token_id).any():
+        break
+
+    print(tokenizer.decode(next_token[0]), end="", flush=True)
+print()
+
+# 4. Output result
+print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])
+```
+
+## License
+
+This model is released under the [LFM 1.0 License](LICENSE).

chat_template.jinja

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+{{- 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\n" if ns.system_prompt else "") + "Today's date: " + strftime_now("%Y-%m-%d") + "\n\nList of tools: " + (tools | tojson) -%}
+{%- 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

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+{
+  "architectures": [
+    "Lfm2MoeForCausalLM"
+  ],
+  "bos_token_id": 124894,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "dtype": "bfloat16",
+  "eos_token_id": 124900,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 7168,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2_moe",
+  "moe_intermediate_size": 1792,
+  "norm_eps": 1e-05,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_dense_layers": 2,
+  "num_experts": 32,
+  "num_experts_per_tok": 4,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "pad_token_id": 124893,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "routed_scaling_factor": 1.0,
+  "transformers_version": "5.0.0.dev0",
+  "use_cache": true,
+  "use_expert_bias": true,
+  "vocab_size": 128000,
+  "transformers.js_config": {
+    "use_external_data_format": {
+      "model.onnx": 23,
+      "model_fp16.onnx": 10,
+      "model_q4.onnx": 3,
+      "model_q4f16.onnx": 3,
+      "model_q8.onnx": 22
+    },
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 124894,
+  "eos_token_id": 124900,
+  "pad_token_id": 124893,
+  "transformers_version": "4.54.0"
+}

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