Instructions to use FlameF0X/LFM2.5-1.2B-Thinking-CodeX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlameF0X/LFM2.5-1.2B-Thinking-CodeX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FlameF0X/LFM2.5-1.2B-Thinking-CodeX") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FlameF0X/LFM2.5-1.2B-Thinking-CodeX") model = AutoModelForCausalLM.from_pretrained("FlameF0X/LFM2.5-1.2B-Thinking-CodeX", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FlameF0X/LFM2.5-1.2B-Thinking-CodeX with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FlameF0X/LFM2.5-1.2B-Thinking-CodeX" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlameF0X/LFM2.5-1.2B-Thinking-CodeX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FlameF0X/LFM2.5-1.2B-Thinking-CodeX
- SGLang
How to use FlameF0X/LFM2.5-1.2B-Thinking-CodeX with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "FlameF0X/LFM2.5-1.2B-Thinking-CodeX" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlameF0X/LFM2.5-1.2B-Thinking-CodeX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "FlameF0X/LFM2.5-1.2B-Thinking-CodeX" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlameF0X/LFM2.5-1.2B-Thinking-CodeX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FlameF0X/LFM2.5-1.2B-Thinking-CodeX with Docker Model Runner:
docker model run hf.co/FlameF0X/LFM2.5-1.2B-Thinking-CodeX
Training in progress, step 1
Browse files- README.md +59 -0
- chat_template.jinja +45 -0
- config.json +61 -0
- generation_config.json +9 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +20 -0
- training_args.bin +3 -0
README.md
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---
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base_model: LiquidAI/LFM2.5-1.2B-Thinking
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library_name: transformers
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model_name: LFM2.5-1.2B-Thinking-CodeX
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tags:
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- generated_from_trainer
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- sft
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- trackio:https://FlameF0X-trackio.hf.space?project=huggingface&runs=FlameF0X-1777186800&sidebar=collapsed
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- trl
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licence: license
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---
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# Model Card for LFM2.5-1.2B-Thinking-CodeX
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This model is a fine-tuned version of [LiquidAI/LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="FlameF0X/LFM2.5-1.2B-Thinking-CodeX", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" width="150" height="24"/>](https://FlameF0X-trackio.hf.space?project=huggingface&runs=FlameF0X-1777186800&sidebar=collapsed)
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This model was trained with SFT.
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### Framework versions
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- TRL: 1.2.0
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- Transformers: 5.0.0
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.4
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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chat_template.jinja
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{{- bos_token -}}
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{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
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{%- set ns = namespace(system_prompt="") -%}
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{%- if messages[0]["role"] == "system" -%}
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{%- set ns.system_prompt = messages[0]["content"] -%}
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{%- set messages = messages[1:] -%}
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{%- endif -%}
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{%- if tools -%}
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{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
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{%- for tool in tools -%}
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{%- if tool is not string -%}
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{%- set tool = tool | tojson -%}
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{%- endif -%}
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{%- set ns.system_prompt = ns.system_prompt + tool -%}
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{%- if not loop.last -%}
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{%- set ns.system_prompt = ns.system_prompt + ", " -%}
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{%- endif -%}
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{%- endfor -%}
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{%- set ns.system_prompt = ns.system_prompt + "]" -%}
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{%- endif -%}
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{%- if ns.system_prompt -%}
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{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
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{%- endif -%}
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{%- set ns.last_assistant_index = -1 -%}
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{%- for message in messages -%}
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{%- if message["role"] == "assistant" -%}
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{%- set ns.last_assistant_index = loop.index0 -%}
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{%- endif -%}
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{%- endfor -%}
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{%- for message in messages -%}
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{{- "<|im_start|>" + message["role"] + "\n" -}}
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{%- set content = message["content"] -%}
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{%- if content is not string -%}
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{%- set content = content | tojson -%}
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{%- endif -%}
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{%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
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{%- if "</think>" in content -%}
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{%- set content = content.split("</think>")[-1] | trim -%}
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{%- endif -%}
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{%- endif -%}
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{{- content + "<|im_end|>\n" -}}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- "<|im_start|>assistant\n" -}}
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{%- endif -%}
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config.json
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{
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"architectures": [
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"Lfm2ForCausalLM"
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],
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"block_auto_adjust_ff_dim": true,
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"block_dim": 2048,
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| 7 |
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"block_ff_dim": 12288,
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| 8 |
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"block_ffn_dim_multiplier": 1.0,
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"block_mlp_init_scale": 1.0,
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"block_multiple_of": 256,
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"block_norm_eps": 1e-05,
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"block_out_init_scale": 1.0,
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"block_use_swiglu": true,
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"block_use_xavier_init": true,
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"bos_token_id": 1,
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| 16 |
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"conv_L_cache": 3,
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| 17 |
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"conv_bias": false,
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| 18 |
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"conv_dim": 2048,
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| 19 |
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"conv_use_xavier_init": true,
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"dtype": "float32",
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| 21 |
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"eos_token_id": 7,
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| 22 |
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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| 37 |
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"conv",
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"full_attention",
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| 39 |
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"conv",
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"full_attention",
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"conv"
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],
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| 43 |
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"max_position_embeddings": 128000,
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| 44 |
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"model_type": "lfm2",
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| 45 |
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"norm_eps": 1e-05,
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| 46 |
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"num_attention_heads": 32,
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| 47 |
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"num_heads": 32,
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| 48 |
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"num_hidden_layers": 16,
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| 49 |
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"num_key_value_heads": 8,
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| 50 |
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"pad_token_id": 0,
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| 51 |
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"rope_parameters": {
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| 52 |
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"rope_theta": 1000000.0,
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| 53 |
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"rope_type": "default"
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| 54 |
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},
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| 55 |
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"tie_embedding": true,
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| 56 |
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"tie_word_embeddings": true,
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| 57 |
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"transformers_version": "5.0.0",
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| 58 |
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"use_cache": false,
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| 59 |
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"use_pos_enc": true,
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| 60 |
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"vocab_size": 65536
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| 61 |
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}
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generation_config.json
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{
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"_from_model_config": true,
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| 3 |
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"bos_token_id": 1,
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| 4 |
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"eos_token_id": [
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| 5 |
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7
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| 6 |
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],
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| 7 |
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"pad_token_id": 0,
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| 8 |
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"transformers_version": "5.0.0"
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| 9 |
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:57d7796cdfc3045d532b03eaab4065aa23d025888eb724493ad7f47969890377
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size 4681379080
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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| 2 |
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"backend": "tokenizers",
|
| 3 |
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"bos_token": "<|startoftext|>",
|
| 4 |
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"clean_up_tokenization_spaces": false,
|
| 5 |
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"eos_token": "<|im_end|>",
|
| 6 |
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"is_local": false,
|
| 7 |
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"legacy": false,
|
| 8 |
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"model_input_names": [
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| 9 |
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"input_ids",
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| 10 |
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"attention_mask"
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| 11 |
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],
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| 12 |
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"model_max_length": 1000000000000000019884624838656,
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| 13 |
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"model_specific_special_tokens": {},
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| 14 |
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"pad_token": "<|pad|>",
|
| 15 |
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"sp_model_kwargs": {},
|
| 16 |
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"spaces_between_special_tokens": false,
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| 17 |
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"tokenizer_class": "TokenizersBackend",
|
| 18 |
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"use_default_system_prompt": false,
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| 19 |
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"use_fast": true
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| 20 |
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}
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training_args.bin
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:82c6004dc508e14b5a210703f3865a66e1890d174c10985a2a987889da0cba74
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| 3 |
+
size 5713
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