Instructions to use schonsense/Bragi_v2_70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schonsense/Bragi_v2_70B with Transformers:

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

pipe = pipeline("text-generation", model="schonsense/Bragi_v2_70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("schonsense/Bragi_v2_70B")
model = AutoModelForCausalLM.from_pretrained("schonsense/Bragi_v2_70B")
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 schonsense/Bragi_v2_70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schonsense/Bragi_v2_70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "schonsense/Bragi_v2_70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/schonsense/Bragi_v2_70B

SGLang

How to use schonsense/Bragi_v2_70B 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 "schonsense/Bragi_v2_70B" \
    --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": "schonsense/Bragi_v2_70B",
		"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 "schonsense/Bragi_v2_70B" \
        --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": "schonsense/Bragi_v2_70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use schonsense/Bragi_v2_70B with Docker Model Runner:
```
docker model run hf.co/schonsense/Bragi_v2_70B
```

bragi4

Better slop handling at the expense of less standard format output.

Look to the Loki v2 repo for prompts and samplers, they transfer well.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: CrucibleLab/L3.3-70B-Loki-V2.0
  - model: schonsense/Tropoplectic
merge_method: slerp
base_model: schonsense/Tropoplectic
parameters:
  t:
    - filter: q_proj
      value: [0.3, 0.30, 0.30, 0.30, 0.8]
    - filter: k_proj
      value: [0.2, 0.20, 0.20, 0.20, 0.7]
    - filter: v_proj
      value: [0.4, 0.40, 0.40, 0.40, 0.8]
    - filter: o_proj
      value: [0.5, 0.75, 0.75, 0.75, 0.9] 
    - filter: gate_proj
      value: [0.20, 0.20, 0.20, 0.20, 0.8]
    - filter: up_proj
      value: [0.30, 0.30, 0.30, 0.30, 0.8]
    - filter: down_proj
      value: [0.50, 0.75, 0.75, 0.75, 0.9]
    - filter: lm_head
      value: 0.5
    - value: 0

  normalize: false
  int8_mask: false
  rescale: false

dtype: float32
out_dtype: bfloat16

chat_template: llama3
tokenizer:
  source: union
  pad_to_multiple_of: 8

Downloads last month: 5

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for schonsense/Bragi_v2_70B

CrucibleLab/L3.3-70B-Loki-V2.0

schonsense/Tropoplectic

Merge model

this model

Merges

1 model