Instructions to use ashishnair/Llama-Ione-8B-roleplay-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ashishnair/Llama-Ione-8B-roleplay-v1 with Transformers:

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

pipe = pipeline("text-generation", model="ashishnair/Llama-Ione-8B-roleplay-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ashishnair/Llama-Ione-8B-roleplay-v1")
model = AutoModelForCausalLM.from_pretrained("ashishnair/Llama-Ione-8B-roleplay-v1")
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]:]))

Inference
Local Apps Settings

vLLM

How to use ashishnair/Llama-Ione-8B-roleplay-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ashishnair/Llama-Ione-8B-roleplay-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ashishnair/Llama-Ione-8B-roleplay-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ashishnair/Llama-Ione-8B-roleplay-v1

SGLang

How to use ashishnair/Llama-Ione-8B-roleplay-v1 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 "ashishnair/Llama-Ione-8B-roleplay-v1" \
    --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": "ashishnair/Llama-Ione-8B-roleplay-v1",
		"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 "ashishnair/Llama-Ione-8B-roleplay-v1" \
        --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": "ashishnair/Llama-Ione-8B-roleplay-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ashishnair/Llama-Ione-8B-roleplay-v1 with Docker Model Runner:
```
docker model run hf.co/ashishnair/Llama-Ione-8B-roleplay-v1
```

ashishnair commited on Apr 9

Commit

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@@ -185,11 +185,11 @@ The `moral_scenarios` drop is the most significant. MMLU moral scenarios test ri
 | Stage | Action | Output | Loss |
 |-------|--------|--------|------|
-| 1 | DARE-TIES merge: `Llama-3.1-8B` (w:0.3/d:0.5) + `self-after-dark` (w:0.7/d:0.8) | `merged_weird1` | - |
-| 2 | SFT on 2,000-sample human dialogue corpus | `merged_weird1_sft` | 1.7368 |
-| 3 | DARE-TIES merge: `merged_weird1_sft` (w:0.7/d:0.8) + `Llama-3.1-8B-Instruct` (w:0.3/d:0.5) | `merged_B_weird2` | - |
-| 4 | SFT on 900-sample GPT multi-persona instruction dataset | `merged_weird2_sft1` | 1.1821 |
-| 5 | SFT on dialogue corpus (re-grounding pass) | `Llama-Ione-8B-roleplay-v1` | 1.4733 |
 | Stat | Value |
 |------|-------|

 | Stage | Action | Output | Loss |
 |-------|--------|--------|------|
+| 1 | DARE-TIES merge: `Llama-3.1-8B` (w:0.3/d:0.5) + `self-after-dark` (w:0.7/d:0.8) | - |
+| 2 | SFT on 2,000-sample human dialogue corpus | 1.7368 |
+| 3 | DARE-TIES merge: `merged_weird1_sft` (w:0.7/d:0.8) + `Llama-3.1-8B-Instruct` (w:0.3/d:0.5) | - |
+| 4 | SFT on 900-sample GPT multi-persona instruction dataset | 1.1821 |
+| 5 | SFT on dialogue corpus (re-grounding pass) | 1.4733 |
 | Stat | Value |
 |------|-------|