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Model: spitfire4794/LFM2.5-1.2B-Instruct-Heretic Source: Original Platform
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---
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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language:
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- en
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- ar
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- zh
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- fr
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- de
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- ja
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- ko
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- es
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pipeline_tag: text-generation
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tags:
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- liquid
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- lfm2.5
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- edge
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- heretic
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- uncensored
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- decensored
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- abliterated
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base_model:
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- LiquidAI/LFM2.5-1.2B-Instruct
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---
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# This is a decensored version of [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct), made using [Heretic](https://github.com/p-e-w/heretic) v1.1.0
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## Abliteration parameters
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| Parameter | Value |
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| :-------- | :---: |
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| **direction_index** | 8.14 |
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| **attn.o_proj.max_weight** | 1.07 |
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| **attn.o_proj.max_weight_position** | 9.05 |
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| **attn.o_proj.min_weight** | 0.90 |
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| **attn.o_proj.min_weight_distance** | 8.96 |
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| **mlp.down_proj.max_weight** | 1.43 |
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| **mlp.down_proj.max_weight_position** | 11.39 |
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| **mlp.down_proj.min_weight** | 1.27 |
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| **mlp.down_proj.min_weight_distance** | 8.82 |
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## Performance
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| Metric | This model | Original model ([LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)) |
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| :----- | :--------: | :---------------------------: |
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| **KL divergence** | 0.7207 | 0 *(by definition)* |
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| **Refusals** | 10/100 | 98/100 |
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-----
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<div align="center">
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<img
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src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
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alt="Liquid AI"
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style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
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/>
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<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
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<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
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<a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> •
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<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a>
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</div>
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</div>
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# LFM2.5-1.2B-Instruct
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LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
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- **Best-in-class performance**: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.
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- **Fast edge inference**: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
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- **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.
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Find more information about LFM2.5 in our [blog post](https://www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai).
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## 🗒️ Model Details
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| Model | Parameters | Description |
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|-------|------------|-------------|
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| [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) | 1.2B | Pre-trained base model for fine-tuning |
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| [**LFM2.5-1.2B-Instruct**](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | 1.2B | General-purpose instruction-tuned model |
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| [LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking) | 1.2B | General-purpose reasoning model |
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| [LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) | 1.2B | Japanese-optimized chat model |
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| [LFM2.5-VL-1.6B](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | 1.6B | Vision-language model with fast inference |
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| [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) | 1.5B | Audio-language model for speech and text I/O |
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LFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features:
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- **Number of parameters**: 1.17B
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- **Number of layers**: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
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- **Training budget**: 28T tokens
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- **Context length**: 32,768 tokens
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- **Vocabulary size**: 65,536
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- **Knowledge cutoff**: Mid-2024
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- **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish
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- **Generation parameters**:
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- `temperature: 0.1`
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- `top_k: 50`
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- `top_p: 0.1`
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- `repetition_penalty: 1.05`
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| Model | Description |
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|-------|-------------|
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| [**LFM2.5-1.2B-Instruct**](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. |
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| [LFM2.5-1.2B-Instruct-GGUF](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. |
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| [LFM2.5-1.2B-Instruct-ONNX](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-ONNX) | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). |
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| [LFM2.5-1.2B-Instruct-MLX](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. |
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We recommend using it for agentic tasks, data extraction, and RAG. It is not recommended for knowledge-intensive tasks and programming.
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### Chat Template
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LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:
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```
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<|startoftext|><|im_start|>system
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You are a helpful assistant trained by Liquid AI.<|im_end|>
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<|im_start|>user
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What is C. elegans?<|im_end|>
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<|im_start|>assistant
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```
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You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.
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### Tool Use
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LFM2.5 supports function calling as follows:
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1. **Function definition**: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) function with tools.
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2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
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3. **Function execution**: The function call is executed, and the result is returned as a "tool" role.
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4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
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See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example:
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```
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<|startoftext|><|im_start|>system
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List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
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<|im_start|>user
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What is the current status of candidate ID 12345?<|im_end|>
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<|im_start|>assistant
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<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
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<|im_start|>tool
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[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
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<|im_start|>assistant
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The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>
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```
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## 🏃 Inference
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LFM2.5 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
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| Name | Description | Docs | Notebook |
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|------|-------------|------|:--------:|
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| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — |
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| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> | — |
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Here's a quick start example with Transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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model_id = "LiquidAI/LFM2.5-1.2B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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dtype="bfloat16",
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# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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prompt = "What is C. elegans?"
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input_ids = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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add_generation_prompt=True,
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return_tensors="pt",
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tokenize=True,
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).to(model.device)
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output = model.generate(
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input_ids,
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do_sample=True,
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temperature=0.1,
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top_k=50,
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top_p=0.1,
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repetition_penalty=1.05,
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max_new_tokens=512,
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streamer=streamer,
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)
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```
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## 🔧 Fine-Tuning
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We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.
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| Name | Description | Docs | Notebook |
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|------|-------------|------|----------|
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| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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||||||
|
| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
|
||||||
|
| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
|
||||||
|
|
||||||
|
## 📊 Performance
|
||||||
|
|
||||||
|
### Benchmarks
|
||||||
|
|
||||||
|
We compared LFM2.5-1.2B-Instruct with relevant sub-2B models on a diverse suite of benchmarks.
|
||||||
|
|
||||||
|
| Model | GPQA | MMLU-Pro | IFEval | IFBench | Multi-IF | AIME25 | BFCLv3 |
|
||||||
|
|-------|------|----------|--------|---------|----------|--------|--------|
|
||||||
|
| **LFM2.5-1.2B-Instruct** | 38.89 | 44.35 | 86.23 | 47.33 | 60.98 | 14.00 | 49.12 |
|
||||||
|
| Qwen3-1.7B (instruct)| 34.85 | 42.91 | 73.68 | 21.33 | 56.48 | 9.33 | 46.30 |
|
||||||
|
| Granite 4.0-1B | 24.24 | 33.53 | 79.61 | 21.00 | 43.65 | 3.33 | 52.43 |
|
||||||
|
| Llama 3.2 1B Instruct | 16.57 | 20.80 | 52.37 | 15.93 | 30.16 | 0.33 | 21.44 |
|
||||||
|
| Gemma 3 1B IT | 24.24 | 14.04 | 63.25 | 20.47 | 44.31 | 1.00 | 16.64 |
|
||||||
|
|
||||||
|
GPQA, MMLU-Pro, IFBench, and AIME25 follow [ArtificialAnalysis's methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking). For IFEval and Multi-IF, we report the average score across strict and loose prompt and instruction accuracies. For BFCLv3, we report the final weighted average score with a custom Liquid handler to support our tool use template.
|
||||||
|
|
||||||
|
### Inference speed
|
||||||
|
|
||||||
|
LFM2.5-1.2B-Instruct offers extremely fast inference speed on CPUs with a low memory profile compared to similar-sized models.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
In addition, we are partnering with AMD, Qualcomm, and Nexa AI to bring the LFM2.5 family to NPUs. These optimized models are available through our partners, enabling highly efficient on-device inference.
|
||||||
|
The following numbers have been calculated using 1K prefill and 100 decode tokens:
|
||||||
|
|
||||||
|
| Device | Inference | Framework | Model | Prefill (tok/s) | Decode (tok/s) | Memory (GB) |
|
||||||
|
| ---------------------------------------------------- | --------- | ---------------- | -------------------- | --------------- | -------------- | ----------- |
|
||||||
|
| Qualcomm Snapdragon® X Elite | NPU | NexaML | LFM2.5-1.2B-Instruct | 2591 | 63 | 0.9GB |
|
||||||
|
| Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro) | NPU | NexaML | LFM2.5-1.2B-Instruct | 4391 | 82 | 0.9GB |
|
||||||
|
| Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU | llama.cpp (Q4_0) | LFM2.5-1.2B-Instruct | 335 | 70 | 719MB |
|
||||||
|
| Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) | CPU | llama.cpp (Q4_0) | Qwen3-1.7B | 181 | 40 | 1306MB |
|
||||||
|
|
||||||
|
These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.
|
||||||
|
|
||||||
|
## Contact
|
||||||
|
|
||||||
|
For enterprise solutions and edge deployment, contact [sales@liquid.ai](mailto:sales@liquid.ai).
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@article{liquidai2025lfm2,
|
||||||
|
title={LFM2 Technical Report},
|
||||||
|
author={Liquid AI},
|
||||||
|
journal={arXiv preprint arXiv:2511.23404},
|
||||||
|
year={2025}
|
||||||
|
}
|
||||||
|
```
|
||||||
45
chat_template.jinja
Normal file
45
chat_template.jinja
Normal file
@@ -0,0 +1,45 @@
|
|||||||
|
{{- bos_token -}}
|
||||||
|
{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
|
||||||
|
{%- set ns = namespace(system_prompt="") -%}
|
||||||
|
{%- if messages[0]["role"] == "system" -%}
|
||||||
|
{%- set ns.system_prompt = messages[0]["content"] -%}
|
||||||
|
{%- 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 -%}
|
||||||
|
{%- set ns.last_assistant_index = -1 -%}
|
||||||
|
{%- for message in messages -%}
|
||||||
|
{%- if message["role"] == "assistant" -%}
|
||||||
|
{%- set ns.last_assistant_index = loop.index0 -%}
|
||||||
|
{%- endif -%}
|
||||||
|
{%- endfor -%}
|
||||||
|
{%- for message in messages -%}
|
||||||
|
{{- "<|im_start|>" + message["role"] + "\n" -}}
|
||||||
|
{%- set content = message["content"] -%}
|
||||||
|
{%- if content is not string -%}
|
||||||
|
{%- set content = content | tojson -%}
|
||||||
|
{%- endif -%}
|
||||||
|
{%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
|
||||||
|
{%- if "</think>" in content -%}
|
||||||
|
{%- set content = content.split("</think>")[-1] | trim -%}
|
||||||
|
{%- endif -%}
|
||||||
|
{%- endif -%}
|
||||||
|
{{- content + "<|im_end|>\n" -}}
|
||||||
|
{%- endfor -%}
|
||||||
|
{%- if add_generation_prompt -%}
|
||||||
|
{{- "<|im_start|>assistant\n" -}}
|
||||||
|
{%- endif -%}
|
||||||
57
config.json
Normal file
57
config.json
Normal file
@@ -0,0 +1,57 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Lfm2ForCausalLM"
|
||||||
|
],
|
||||||
|
"block_auto_adjust_ff_dim": true,
|
||||||
|
"block_dim": 2048,
|
||||||
|
"block_ff_dim": 12288,
|
||||||
|
"block_ffn_dim_multiplier": 1.0,
|
||||||
|
"block_mlp_init_scale": 1.0,
|
||||||
|
"block_multiple_of": 256,
|
||||||
|
"block_norm_eps": 1e-05,
|
||||||
|
"block_out_init_scale": 1.0,
|
||||||
|
"block_use_swiglu": true,
|
||||||
|
"block_use_xavier_init": true,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"conv_L_cache": 3,
|
||||||
|
"conv_bias": false,
|
||||||
|
"conv_dim": 2048,
|
||||||
|
"conv_use_xavier_init": true,
|
||||||
|
"dtype": "bfloat16",
|
||||||
|
"eos_token_id": 7,
|
||||||
|
"hidden_size": 2048,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 12288,
|
||||||
|
"layer_types": [
|
||||||
|
"conv",
|
||||||
|
"conv",
|
||||||
|
"full_attention",
|
||||||
|
"conv",
|
||||||
|
"conv",
|
||||||
|
"full_attention",
|
||||||
|
"conv",
|
||||||
|
"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": 32,
|
||||||
|
"num_heads": 32,
|
||||||
|
"num_hidden_layers": 16,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"tie_embedding": true,
|
||||||
|
"transformers_version": "4.57.1",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_pos_enc": true,
|
||||||
|
"vocab_size": 65536
|
||||||
|
}
|
||||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 7,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"transformers_version": "4.57.1"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:462c1d96b09c8611e33c4e123b438244def5dc525ef5610654f0b307fdfc4d3a
|
||||||
|
size 2340697936
|
||||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|startoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
323830
tokenizer.json
Normal file
323830
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
4094
tokenizer_config.json
Normal file
4094
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user