Instructions to use timpal0l/Mistral-7B-v0.1-flashback-v2-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timpal0l/Mistral-7B-v0.1-flashback-v2-instruct with Transformers:

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

pipe = pipeline("text-generation", model="timpal0l/Mistral-7B-v0.1-flashback-v2-instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timpal0l/Mistral-7B-v0.1-flashback-v2-instruct")
model = AutoModelForCausalLM.from_pretrained("timpal0l/Mistral-7B-v0.1-flashback-v2-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use timpal0l/Mistral-7B-v0.1-flashback-v2-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timpal0l/Mistral-7B-v0.1-flashback-v2-instruct

SGLang

How to use timpal0l/Mistral-7B-v0.1-flashback-v2-instruct 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 "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct" \
    --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": "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct",
		"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 "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct" \
        --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": "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timpal0l/Mistral-7B-v0.1-flashback-v2-instruct with Docker Model Runner:
```
docker model run hf.co/timpal0l/Mistral-7B-v0.1-flashback-v2-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🐈‍⬛ Mistral-7B-v0.1-flashback-v2-instruct

Mistral-7B-v0.1-flashback-v2-instruct is an instruct based version of the base model timpal0l/Mistral-7B-v0.1-flashback-v2. It has been finetuned on a the machine translated instruct dataset OpenHermes2.5.

How to use:

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    "timpal0l/Mistral-7B-v0.1-flashback-v2-instruct",
    device_map="auto"
)

text = """
Hur många ägg har jag? Jag hade 10 ägg, sen gav jag bort 5 ägg.
Sen fick jag 3 ägg av en kompis.
"""

generated = pipe(f"USER:{text}ASSISTANT:", max_length=512, temperature=0.6)
print(generated[0]["generated_text"].split("ASSISTANT: ")[1:][0])

Output:

Du har 8 ägg. Här är resonemanget:
1. Du börjar med 10 ägg
2. Du ger bort 5 ägg, vilket lämnar dig med 10 - 5 = 5 ägg
3. Sedan får du 3 ägg av en kompis, vilket gör att du har 5 + 3 = 8 ägg.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for timpal0l/Mistral-7B-v0.1-flashback-v2-instruct

Merges

6 models

Quantizations

3 models

timpal0l
/

Mistral-7B-v0.1-flashback-v2-instruct

🐈‍⬛ Mistral-7B-v0.1-flashback-v2-instruct

How to use:

Model tree for timpal0l/Mistral-7B-v0.1-flashback-v2-instruct

Datasets used to train timpal0l/Mistral-7B-v0.1-flashback-v2-instruct