Text Generation
Transformers
Safetensors
GGUF
English
falcon_h1
palmer
text-editing
rewriting
paraphrasing
grammar-correction
edge
small-language-model
Instructions to use appvoid/palmer-006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/palmer-006 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/palmer-006")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-006") model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-006", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use appvoid/palmer-006 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 appvoid/palmer-006 # Run inference directly in the terminal: llama cli -hf appvoid/palmer-006
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf appvoid/palmer-006 # Run inference directly in the terminal: llama cli -hf appvoid/palmer-006
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 appvoid/palmer-006 # Run inference directly in the terminal: ./llama-cli -hf appvoid/palmer-006
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 appvoid/palmer-006 # Run inference directly in the terminal: ./build/bin/llama-cli -hf appvoid/palmer-006
Use Docker
docker model run hf.co/appvoid/palmer-006
- LM Studio
- Jan
- vLLM
How to use appvoid/palmer-006 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/palmer-006" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/palmer-006", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/palmer-006
- SGLang
How to use appvoid/palmer-006 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 "appvoid/palmer-006" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/palmer-006", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "appvoid/palmer-006" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/palmer-006", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use appvoid/palmer-006 with Ollama:
ollama run hf.co/appvoid/palmer-006
- Unsloth Studio
How to use appvoid/palmer-006 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 appvoid/palmer-006 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 appvoid/palmer-006 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for appvoid/palmer-006 to start chatting
- Docker Model Runner
How to use appvoid/palmer-006 with Docker Model Runner:
docker model run hf.co/appvoid/palmer-006
- Lemonade
How to use appvoid/palmer-006 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull appvoid/palmer-006
Run and chat with the model
lemonade run user.palmer-006-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| { | |
| "architectures": [ | |
| "FalconH1ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attention_in_multiplier": 1.0, | |
| "attention_out_multiplier": 1.0, | |
| "attn_layer_indices": null, | |
| "bos_token_id": 1, | |
| "dtype": "bfloat16", | |
| "embedding_multiplier": 0.11083984375, | |
| "eos_token_id": 11, | |
| "expansion_factor": 1.5, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 768, | |
| "key_multiplier": 1.0, | |
| "lm_head_multiplier": 0.078125, | |
| "mamba_chunk_size": 32, | |
| "mamba_conv_bias": true, | |
| "mamba_d_conv": 4, | |
| "mamba_d_head": 32, | |
| "mamba_d_ssm": 768, | |
| "mamba_d_state": 64, | |
| "mamba_expand": 2, | |
| "mamba_n_groups": 1, | |
| "mamba_n_heads": 24, | |
| "mamba_norm_before_gate": false, | |
| "mamba_proj_bias": false, | |
| "mamba_rms_norm": false, | |
| "mamba_use_mlp": true, | |
| "max_position_embeddings": 262144, | |
| "mlp_bias": false, | |
| "mlp_expansion_factor": 8, | |
| "mlp_multipliers": [ | |
| 1.0, | |
| 1.0 | |
| ], | |
| "model_type": "falcon_h1", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 2, | |
| "num_logits_to_keep": 1, | |
| "pad_token_id": 0, | |
| "projectors_bias": false, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 100000000000.0, | |
| "sliding_window": null, | |
| "ssm_in_multiplier": 1.0, | |
| "ssm_multipliers": [ | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "ssm_out_multiplier": 1.0, | |
| "tie_word_embeddings": true, | |
| "time_step_floor": 0.0001, | |
| "time_step_max": 0.1, | |
| "time_step_min": 0.001, | |
| "time_step_rank": "auto", | |
| "transformers_version": "4.57.0", | |
| "use_cache": false, | |
| "vocab_size": 32768 | |
| } | |