Instructions to use HaileyStorm/llama3-5.4b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HaileyStorm/llama3-5.4b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="HaileyStorm/llama3-5.4b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HaileyStorm/llama3-5.4b-instruct")
model = AutoModelForCausalLM.from_pretrained("HaileyStorm/llama3-5.4b-instruct", device_map="auto")
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 Settings

vLLM

How to use HaileyStorm/llama3-5.4b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HaileyStorm/llama3-5.4b-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": "HaileyStorm/llama3-5.4b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HaileyStorm/llama3-5.4b-instruct

SGLang

How to use HaileyStorm/llama3-5.4b-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 "HaileyStorm/llama3-5.4b-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": "HaileyStorm/llama3-5.4b-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 "HaileyStorm/llama3-5.4b-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": "HaileyStorm/llama3-5.4b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HaileyStorm/llama3-5.4b-instruct with Docker Model Runner:
```
docker model run hf.co/HaileyStorm/llama3-5.4b-instruct
```

HaileyStorm commited on May 26, 2024

Commit

a1d7289

verified ·

1 Parent(s): a0fbecc

Create README.md

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+---
+base_model:
+- meta-llama/Meta-Llama-3-8B-Instruct
+library_name: transformers
+tags:
+- mergekit
+- prune
+- dpo
+- instruct
+datasets:
+- mlabonne/orpo-dpo-mix-40k
+license: llama3
+pipeline_tag: text-generation
+model-index:
+  - name: llama3-5.4b-instruct
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          name: truthfulqa_mc2
+          type: truthfulqa_mc2
+        metrics:
+          - name: TruthfulQA (0-Shot)
+            type: TruthfulQA (0-Shot)
+            value: 0.517686926475562
+      - task:
+          type: text-generation
+        dataset:
+          name: ai2_arc
+          type: ai2_arc
+        metrics:
+          - name: AI2 Reasoning Challenge (25-Shot)
+            type: AI2 Reasoning Challenge (25-Shot)
+            value: 0.360068259385666
+      - task:
+          type: text-generation
+        dataset:
+          name: hellaswag
+          type: hellaswag
+        metrics:
+          - name: HellaSwag (10-Shot)
+            type: HellaSwag (10-Shot)
+            value: 0.503485361481777
+      - task:
+          type: text-generation
+        dataset:
+          name: winogrande
+          type: winogrande
+        metrics:
+          - name: Winogrande (5-Shot)
+            type: Winogrande (5-Shot)
+            value: 0.633780584056827
+      - task:
+          type: text-generation
+        dataset:
+          name: mmlu
+          type: mmlu
+        metrics:
+          - name: MMLU (5-Shot)
+            type: MMLU (5-Shot)
+            value: 0.290912975359635
+---
+# GGUFs
+Quantized versions of this model are available:
+https://huggingface.co/HaileyStorm/llama3-5.4b-instruct-Q8_0-GGUF
+https://huggingface.co/HaileyStorm/llama3-5.4b-instruct-Q6_K-GGUF
+https://huggingface.co/HaileyStorm/llama3-5.4b-instruct-Q5_K_M-GGUF
+https://huggingface.co/HaileyStorm/llama3-5.4b-instruct-Q4_0-GGUF
+# Pruned & Tuned
+This is a "merge" of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+It is a prune of Meta-Llama-3-8B-Instruct from 32 layers down to 20, or about 5.4B parameter -- it's about 67% the size of the original.
+Mostly, this is a test of (significant) pruning & healing an instruct-tuned model.
+## Healing / Finetune
+I healed the model by doing a full weight DPO finetune for 139k samples (3.15 epochs), and then a LoRA with r=128 a=256 for 73k samples (1.67 epochs).
+Prior to healing, the model returned absolute gibberish to any prompt, rarely two real words together. For example, give "2+2=" it might return "Mahmisan Pannpyout Na RMITa CMI TTi GP BP GP RSi TBi DD PS..."
+The results are pretty good! The model has issues, but could have legitimate uses. It carry on a conversation. It's certainly usable, if not useful.
+Truthfulness and commonsense reasoning suffered the least from the prune / were healed the best. Knowledge and complex reasoning suffered the most.
+This model has 67% the parameters of the original, and has:
+~100% the TruthfulQA score of the original
+~60% the ARC Challenge score
+~65% the Hellaswag score
+~85% the Winogrande score
+~45% the the MMLU score
+### Benchmarks
+{Benchmark images on their way...}
+## Why 5.4B?
+This size should allow for:
+- bf16 inference on 24GB VRAM
+- Q8 or Q6 inference on 6GB VRAM
+- Q5 inference on 4GB VRAM
+- Fine-tuning on ... well, with less VRAM than an 8B model
+And of course, as stated, it was a test of significant pruning, and of pruning&healing an instruct-tuned model. As a test, I think it's definitely successful.
+## Mergekit Details
+### Merge Method
+This model was merged using the passthrough merge method.
+### Models Merged
+The following models were included in the merge:
+* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+dtype: bfloat16
+merge_method: passthrough
+slices:
+- sources:
+  - layer_range: [0, 16]
+    model: meta-llama/Meta-Llama-3-8B-Instruct
+- sources:
+  - layer_range: [20, 21]
+    model: meta-llama/Meta-Llama-3-8B-Instruct
+- sources:
+  - layer_range: [29, 32]
+    model: meta-llama/Meta-Llama-3-8B-Instruct
+```