Instructions to use Lambent/threebird-scribe-alpha0.2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lambent/threebird-scribe-alpha0.2-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lambent/threebird-scribe-alpha0.2-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Lambent/threebird-scribe-alpha0.2-7B") model = AutoModelForCausalLM.from_pretrained("Lambent/threebird-scribe-alpha0.2-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Lambent/threebird-scribe-alpha0.2-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Lambent/threebird-scribe-alpha0.2-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lambent/threebird-scribe-alpha0.2-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Lambent/threebird-scribe-alpha0.2-7B
- SGLang
How to use Lambent/threebird-scribe-alpha0.2-7B 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 "Lambent/threebird-scribe-alpha0.2-7B" \ --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": "Lambent/threebird-scribe-alpha0.2-7B", "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 "Lambent/threebird-scribe-alpha0.2-7B" \ --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": "Lambent/threebird-scribe-alpha0.2-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Lambent/threebird-scribe-alpha0.2-7B with Docker Model Runner:
docker model run hf.co/Lambent/threebird-scribe-alpha0.2-7B
nextbird
This is a merge of pre-trained language models created using mergekit.
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| eq_bench | 2.1 | none | 0 | eqbench | ↑ | 78.7229 | ± | 1.4751 |
| none | 0 | percent_parseable | ↑ | 100.0000 | ± | 0.0000 |
Slight improvement over both constituents on EQ-Bench benchmark, again rivaling senseable/WestLake-7B-v2 ancestor while remaining able to be parsed.
Lambent/threebird-scribe-alpha0.1-7B is a local mix of fine-tunes of Lambent/threebird-7B aiming just to fix syntax issues with fairly low forgetting or loss of intelligence.
Merge Details
Merge Method
This model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: Lambent/threebird-scribe-alpha0.1-7B
parameters:
density: 1.0
weight: 1.0
- model: FelixChao/Patronum-7B
parameters:
density: 1.0
weight: 1.0
base_model: mistralai/Mistral-7B-v0.1
merge_method: ties
dtype: float16
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