Text Generation
Transformers
Safetensors
GGUF
llama-cpp-python
MLX
Korean
English
qwen2
finance
korean
stock-analysis
reasoning
dpo
llama-cpp
apple-silicon
4bit
quantized
vllm
ollama
conversational
text-generation-inference
Instructions to use intrect/VELA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intrect/VELA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="intrect/VELA") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("intrect/VELA") model = AutoModelForCausalLM.from_pretrained("intrect/VELA") 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]:])) - llama-cpp-python
How to use intrect/VELA with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="intrect/VELA", filename="vela-dpo-v6-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - MLX
How to use intrect/VELA with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("intrect/VELA") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use intrect/VELA with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf intrect/VELA:Q4_K_M # Run inference directly in the terminal: llama-cli -hf intrect/VELA:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf intrect/VELA:Q4_K_M # Run inference directly in the terminal: llama-cli -hf intrect/VELA: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 intrect/VELA:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf intrect/VELA: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 intrect/VELA:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf intrect/VELA:Q4_K_M
Use Docker
docker model run hf.co/intrect/VELA:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use intrect/VELA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "intrect/VELA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "intrect/VELA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/intrect/VELA:Q4_K_M
- SGLang
How to use intrect/VELA 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 "intrect/VELA" \ --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": "intrect/VELA", "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 "intrect/VELA" \ --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": "intrect/VELA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use intrect/VELA with Ollama:
ollama run hf.co/intrect/VELA:Q4_K_M
- Unsloth Studio new
How to use intrect/VELA 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 intrect/VELA 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 intrect/VELA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for intrect/VELA to start chatting
- Pi new
How to use intrect/VELA with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "intrect/VELA"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "intrect/VELA" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use intrect/VELA with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "intrect/VELA"
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 intrect/VELA
Run Hermes
hermes
- MLX LM
How to use intrect/VELA with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "intrect/VELA"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "intrect/VELA" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "intrect/VELA", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use intrect/VELA with Docker Model Runner:
docker model run hf.co/intrect/VELA:Q4_K_M
- Lemonade
How to use intrect/VELA with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull intrect/VELA:Q4_K_M
Run and chat with the model
lemonade run user.VELA-Q4_K_M
List all available models
lemonade list
fix: replace Qwen default system prompt with VELA identity in chat templates
Browse files- chat_template.jinja +2 -2
- mlx-int4/tokenizer_config.json +3 -2
- tokenizer_config.json +2 -2
chat_template.jinja
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- '
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- '당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\n당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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mlx-int4/tokenizer_config.json
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null,
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"chat_template": ""
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}
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tokenizer_config.json
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},
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"backend": "tokenizers",
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"bos_token": null,
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"split_special_tokens": false,
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}
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"backend": "tokenizers",
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.' }}\n {%- endif %}\n {{- \"\\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\n당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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