Instructions to use strykes/SteraVibeThinker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use strykes/SteraVibeThinker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="strykes/SteraVibeThinker") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("strykes/SteraVibeThinker", dtype="auto") - llama-cpp-python
How to use strykes/SteraVibeThinker with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="strykes/SteraVibeThinker", filename="SteraVibeThinker-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use strykes/SteraVibeThinker with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: llama cli -hf strykes/SteraVibeThinker:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: llama cli -hf strykes/SteraVibeThinker:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf strykes/SteraVibeThinker:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf strykes/SteraVibeThinker:Q4_K_M
Use Docker
docker model run hf.co/strykes/SteraVibeThinker:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use strykes/SteraVibeThinker with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "strykes/SteraVibeThinker" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "strykes/SteraVibeThinker", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/strykes/SteraVibeThinker:Q4_K_M
- SGLang
How to use strykes/SteraVibeThinker 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 "strykes/SteraVibeThinker" \ --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": "strykes/SteraVibeThinker", "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 "strykes/SteraVibeThinker" \ --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": "strykes/SteraVibeThinker", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use strykes/SteraVibeThinker with Ollama:
ollama run hf.co/strykes/SteraVibeThinker:Q4_K_M
- Unsloth Studio
How to use strykes/SteraVibeThinker with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for strykes/SteraVibeThinker to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for strykes/SteraVibeThinker to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for strykes/SteraVibeThinker to start chatting
- Pi
How to use strykes/SteraVibeThinker with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf strykes/SteraVibeThinker:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "strykes/SteraVibeThinker:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use strykes/SteraVibeThinker with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf strykes/SteraVibeThinker:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default strykes/SteraVibeThinker:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use strykes/SteraVibeThinker with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf strykes/SteraVibeThinker:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "strykes/SteraVibeThinker:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use strykes/SteraVibeThinker with Docker Model Runner:
docker model run hf.co/strykes/SteraVibeThinker:Q4_K_M
- Lemonade
How to use strykes/SteraVibeThinker with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull strykes/SteraVibeThinker:Q4_K_M
Run and chat with the model
lemonade run user.SteraVibeThinker-Q4_K_M
List all available models
lemonade list
Add files using upload-large-folder tool
Browse files- .gitattributes +3 -34
- README.md +60 -0
- SteraVibeThinker-Q4_K_M.gguf +3 -0
- raw_weights/chat_template.jinja +54 -0
- raw_weights/config.json +69 -0
- raw_weights/generation_config.json +8 -0
- raw_weights/model.safetensors +3 -0
- raw_weights/tokenizer.json +3 -0
- raw_weights/tokenizer_config.json +16 -0
- raw_weights/training_args.bin +3 -0
- val_meta.jsonl +0 -0
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README.md
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---
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license: mit
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base_model: WeiboAI/VibeThinker-3B
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tags:
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- code
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- agent
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- tool-use
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- gguf
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- qwen2
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library_name: transformers
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pipeline_tag: text-generation
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---
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# SteraVibeThinker
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A full fine-tune of [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B)
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(a 3B reasoning model built on the Qwen2.5-3B / Qwen2.5-Coder-3B architecture) on
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the ~30k-example **Tiny-Giant** agentic tool-use dataset.
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The goal: keep VibeThinker's strong verifiable-reasoning core while teaching it the
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deterministic, Hermes/ChatML-style `<tool_call>` agent format used by the
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Tiny-Giant harness.
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## Files
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| File | Description |
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|---|---|
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| `SteraVibeThinker-Q4_K_M.gguf` | Q4_K_M quantization (~1.8 GB) — for `llama.cpp` / Ollama / LM Studio |
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| `raw_weights/` | Full bf16 safetensors HF checkpoint (re-quantize to any GGUF level from here) |
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| `val_meta.jsonl` | Held-out validation set shipped with the model |
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## Training
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- **Base:** `WeiboAI/VibeThinker-3B` (MIT, Qwen2.5-3B architecture, ChatML-native)
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- **Method:** full fine-tune (not LoRA), bf16 + gradient checkpointing
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- **Data:** ~30k Tiny-Giant agentic tool-use conversations
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- **Epochs:** 2 · **LR:** 7e-6 (cosine, 3% warmup) · **Seq len:** 4096
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- **Loss:** full-sequence (tool results modeled as in-distribution context)
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## Prompt format
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This model was trained with an **explicit ChatML / Hermes renderer**, not
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`tokenizer.apply_chat_template`. Pin the ChatML template explicitly when serving —
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do not rely on auto-detection. Tool calls use:
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```
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<tool_call>
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{"name": "<function-name>", "arguments": {...}}
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</tool_call>
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```
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## Inference (llama.cpp)
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```bash
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llama-cli -m SteraVibeThinker-Q4_K_M.gguf --chat-template chatml
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```
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## License
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MIT, inherited from the VibeThinker-3B base model.
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SteraVibeThinker-Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:0b26426e293bdc609d53d120b1b36ebf44183844d471ce1d716486dc09a48088
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size 1929902176
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raw_weights/chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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| 5 |
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{%- else %}
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| 6 |
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{{- 'You are a helpful assistant.' }}
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{%- endif %}
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| 8 |
+
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 9 |
+
{%- for tool in tools %}
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{{- "\n" }}
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| 11 |
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{{- tool | tojson }}
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{%- endfor %}
|
| 13 |
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 14 |
+
{%- else %}
|
| 15 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 16 |
+
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
| 17 |
+
{%- else %}
|
| 18 |
+
{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
|
| 19 |
+
{%- endif %}
|
| 20 |
+
{%- endif %}
|
| 21 |
+
{%- for message in messages %}
|
| 22 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
| 23 |
+
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
| 24 |
+
{%- elif message.role == "assistant" %}
|
| 25 |
+
{{- '<|im_start|>' + message.role }}
|
| 26 |
+
{%- if message.content %}
|
| 27 |
+
{{- '\n' + message.content }}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{%- for tool_call in message.tool_calls %}
|
| 30 |
+
{%- if tool_call.function is defined %}
|
| 31 |
+
{%- set tool_call = tool_call.function %}
|
| 32 |
+
{%- endif %}
|
| 33 |
+
{{- '\n<tool_call>\n{"name": "' }}
|
| 34 |
+
{{- tool_call.name }}
|
| 35 |
+
{{- '", "arguments": ' }}
|
| 36 |
+
{{- tool_call.arguments | tojson }}
|
| 37 |
+
{{- '}\n</tool_call>' }}
|
| 38 |
+
{%- endfor %}
|
| 39 |
+
{{- '<|im_end|>\n' }}
|
| 40 |
+
{%- elif message.role == "tool" %}
|
| 41 |
+
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
| 42 |
+
{{- '<|im_start|>user' }}
|
| 43 |
+
{%- endif %}
|
| 44 |
+
{{- '\n<tool_response>\n' }}
|
| 45 |
+
{{- message.content }}
|
| 46 |
+
{{- '\n</tool_response>' }}
|
| 47 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 48 |
+
{{- '<|im_end|>\n' }}
|
| 49 |
+
{%- endif %}
|
| 50 |
+
{%- endif %}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{%- if add_generation_prompt %}
|
| 53 |
+
{{- '<|im_start|>assistant\n' }}
|
| 54 |
+
{%- endif %}
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
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|
| 4 |
+
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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"layer_types": [
|
| 14 |
+
"full_attention",
|
| 15 |
+
"full_attention",
|
| 16 |
+
"full_attention",
|
| 17 |
+
"full_attention",
|
| 18 |
+
"full_attention",
|
| 19 |
+
"full_attention",
|
| 20 |
+
"full_attention",
|
| 21 |
+
"full_attention",
|
| 22 |
+
"full_attention",
|
| 23 |
+
"full_attention",
|
| 24 |
+
"full_attention",
|
| 25 |
+
"full_attention",
|
| 26 |
+
"full_attention",
|
| 27 |
+
"full_attention",
|
| 28 |
+
"full_attention",
|
| 29 |
+
"full_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
+
"full_attention",
|
| 32 |
+
"full_attention",
|
| 33 |
+
"full_attention",
|
| 34 |
+
"full_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"full_attention",
|
| 37 |
+
"full_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
+
"full_attention",
|
| 40 |
+
"full_attention",
|
| 41 |
+
"full_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"full_attention",
|
| 44 |
+
"full_attention",
|
| 45 |
+
"full_attention",
|
| 46 |
+
"full_attention",
|
| 47 |
+
"full_attention",
|
| 48 |
+
"full_attention",
|
| 49 |
+
"full_attention"
|
| 50 |
+
],
|
| 51 |
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"max_position_embeddings": 131072,
|
| 52 |
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|
| 53 |
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"model_type": "qwen2",
|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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"rope_parameters": {
|
| 60 |
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"rope_theta": 1000000.0,
|
| 61 |
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"rope_type": "default"
|
| 62 |
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},
|
| 63 |
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|
| 64 |
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"tie_word_embeddings": true,
|
| 65 |
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|
| 66 |
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|
| 67 |
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"use_sliding_window": false,
|
| 68 |
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"vocab_size": 151936
|
| 69 |
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}
|
raw_weights/generation_config.json
ADDED
|
@@ -0,0 +1,8 @@
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|
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|
|
| 1 |
+
{
|
| 2 |
+
"eos_token_id": [
|
| 3 |
+
151643
|
| 4 |
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],
|
| 5 |
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"max_new_tokens": 2048,
|
| 6 |
+
"pad_token_id": 151643,
|
| 7 |
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"transformers_version": "5.12.1"
|
| 8 |
+
}
|
raw_weights/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:5e20a821f0a4b43c7e0797b34dd6956a5e7dbc22c8a118f74e0aeabeeb1fc8c5
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| 3 |
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size 6171927112
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raw_weights/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:b17e16899b7fab7e695509f84bac5f10ed12804f0a590e935941e5af7f092f7f
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| 3 |
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size 11422263
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raw_weights/tokenizer_config.json
ADDED
|
@@ -0,0 +1,16 @@
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|
| 1 |
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{
|
| 2 |
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|
| 3 |
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|
| 4 |
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"bos_token": null,
|
| 5 |
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|
| 6 |
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"eos_token": "<|endoftext|>",
|
| 7 |
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|
| 8 |
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"is_local": false,
|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
raw_weights/training_args.bin
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 5176
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val_meta.jsonl
ADDED
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