Instructions to use rmdhirr/Foxglove_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rmdhirr/Foxglove_7B with Transformers:

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

pipe = pipeline("text-generation", model="rmdhirr/Foxglove_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rmdhirr/Foxglove_7B")
model = AutoModelForMultimodalLM.from_pretrained("rmdhirr/Foxglove_7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rmdhirr/Foxglove_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rmdhirr/Foxglove_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmdhirr/Foxglove_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rmdhirr/Foxglove_7B

SGLang

How to use rmdhirr/Foxglove_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 "rmdhirr/Foxglove_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": "rmdhirr/Foxglove_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 "rmdhirr/Foxglove_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": "rmdhirr/Foxglove_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rmdhirr/Foxglove_7B with Docker Model Runner:
```
docker model run hf.co/rmdhirr/Foxglove_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🌸 Foxglove_7B

Foxglove is a well-rounded RP model. It is smart, does a great job of sticking to character card, and is proficient at following desired markdown.

Foxglove_7B is a merge of the following models using LazyMergekit:

Quantizations

Thanks to mradermacher, static GGUF quants are available here.

Formatting/Preset

Alpaca works best, but Mistral provides good outputs as well.

Configuration

slices:
  - sources:
      - model: ResplendentAI/Datura_7B
        layer_range: [0, 32]
      - model: Epiculous/Mika-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: ResplendentAI/Datura_7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.7, 0.4, 0.6, 1]  
    - filter: mlp
      value: [0.8, 0.5, 0.7, 0.3, 0]  
    - value: 0.6  
dtype: bfloat16

Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "rmdhirr/Foxglove_7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.77
AI2 Reasoning Challenge (25-Shot)	67.83
HellaSwag (10-Shot)	86.57
MMLU (5-Shot)	62.89
TruthfulQA (0-shot)	69.64
Winogrande (5-shot)	80.74
GSM8k (5-shot)	44.96