Instructions to use Lambent/braidbird-scribe-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lambent/braidbird-scribe-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Lambent/braidbird-scribe-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lambent/braidbird-scribe-7B")
model = AutoModelForCausalLM.from_pretrained("Lambent/braidbird-scribe-7B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lambent/braidbird-scribe-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lambent/braidbird-scribe-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lambent/braidbird-scribe-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lambent/braidbird-scribe-7B

SGLang

How to use Lambent/braidbird-scribe-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/braidbird-scribe-7B" \
    --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": "Lambent/braidbird-scribe-7B",
		"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 "Lambent/braidbird-scribe-7B" \
        --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": "Lambent/braidbird-scribe-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lambent/braidbird-scribe-7B with Docker Model Runner:
```
docker model run hf.co/Lambent/braidbird-scribe-7B
```

fourbirdstock

This is a merge of pre-trained language models created using mergekit.

Merge Details

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
eq_bench	2.1	none	0	eqbench	↑	78.7955	±	1.4668
		none	0	percent_parseable	↑	100.0000	±	0.0000

0.3 involved 3 separate tunes stock merged on overlapping datasets for long context writing, multi-turn conversation and RP, with a touch of poetry and code. From there, each of the four threads was separately task-tuned on 2 datasets each. Various methods of combining those via merge were tested, with this one scoring highest on EQ-Bench as an indicator.

My understanding of the Model Stock merge method is that it reduces task adaptation to a significant degree, but also significantly limits forgetting caused by training. I have hope that the adaptation, especially over two stages, is still sufficient to aid in longer contexts and multi-turn conversations from the ancestor models, and add some individual style while retaining a fair amount of their capability.

This model's refusals are ... not nonexistent, but certainly don't rely on them. To my knowledge it has no particular refusal behavior for simply NSFW content, but I haven't exactly exhaustively tested which OSHA violations it will aid and abet.

Merge Method

This model was merged using the Model Stock merge method using Lambent/threebird-scribe-alpha0.3-7B 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/codebird-scribe-7B
  - model: Lambent/songbird-scribe-7B
  - model: Lambent/aetherbird-scribe-7B
  - model: Lambent/bigbird-scribe-7B
base_model: Lambent/threebird-scribe-alpha0.3-7B
merge_method: model_stock
parameters:
  filter_wise: false
tokenizer_source: union
dtype: float16

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F16

Model tree for Lambent/braidbird-scribe-7B

Base model

Lambent/threebird-scribe-alpha0.3-7B

Finetuned

(1)

this model

Datasets used to train Lambent/braidbird-scribe-7B

Paper for Lambent/braidbird-scribe-7B

Model Stock: All we need is just a few fine-tuned models

Paper • 2403.19522 • Published Mar 28, 2024 • 15