Text Generation
MLX
Safetensors
English
gemma2
marketing
digital-marketing
seo
advertising
social-media
thin-language-model
lens
lora
4-bit precision
apple-silicon
conversational
Eval Results (legacy)
Instructions to use FahrenheitResearch/FR-Blaze-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FahrenheitResearch/FR-Blaze-9B 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("FahrenheitResearch/FR-Blaze-9B") 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 Settings
- LM Studio
- MLX LM
How to use FahrenheitResearch/FR-Blaze-9B with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "FahrenheitResearch/FR-Blaze-9B"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "FahrenheitResearch/FR-Blaze-9B" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FahrenheitResearch/FR-Blaze-9B", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- 4d59ccb7a9aeb431b8b89906140e2ed259366209d16088d18553d240cef17949
- Size of remote file:
- 34.4 MB
- SHA256:
- 50fb31adcff3531a5210ed1190f440d41263902efc2b19692cdc3a08dde5f54d
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