Instructions to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="mwitiderrick/open_llama_3b_glaive_code_v0.1")

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

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mwitiderrick/open_llama_3b_glaive_code_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mwitiderrick/open_llama_3b_glaive_code_v0.1

SGLang

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 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 "mwitiderrick/open_llama_3b_glaive_code_v0.1" \
    --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": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"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 "mwitiderrick/open_llama_3b_glaive_code_v0.1" \
        --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": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with Docker Model Runner:
```
docker model run hf.co/mwitiderrick/open_llama_3b_glaive_code_v0.1
```

mwitiderrick commited on Dec 19, 2023

Commit

360bf72

1 Parent(s): 4437aa5

Update README.md

Browse files

Files changed (1) hide show

README.md +14 -53

README.md CHANGED Viewed

@@ -1,13 +1,13 @@
 ---
-base_model: openlm-research/open_llama_3b
 datasets:
 - mwitiderrick/AlpacaCode
 inference: true
 model_type: llama
 prompt_template: |
-  ### Instruction:\n
   {prompt}
-  ### Response:
 created_by: mwitiderrick
 tags:
 - transformers
@@ -28,7 +28,7 @@ model-index:
         metrics:
           - name: hellaswag(0-Shot)
             type: hellaswag (0-Shot)
-            value: 0.6581
       - task:
           type: text-generation
         dataset:
@@ -37,7 +37,7 @@ model-index:
         metrics:
           - name: winogrande(0-Shot)
             type: winogrande (0-Shot)
-            value: 0.6267
       - task:
           type: text-generation
@@ -47,7 +47,7 @@ model-index:
         metrics:
           - name: arc_challenge(0-Shot)
             type: arc_challenge (0-Shot)
-            value: 0.3712
         source:
           name: open_llama_3b_instruct_v_0.2 model card
           url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
@@ -56,69 +56,30 @@ model-index:
 ---
 # OpenLLaMA Code Instruct: An Open Reproduction of LLaMA
-This is an [OpenLlama model](https://huggingface.co/openlm-research/open_llama_3b) that has been fine-tuned on 1 epoch of the
-[AlpacaCode](https://huggingface.co/datasets/mwitiderrick/AlpacaCode) dataset (122K rows).
 ## Prompt Template
 ```
-### Instruction:
-{query}
-### Response:
-<Leave new line for model to respond>
 ```
 ## Usage
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
-tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
-model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
 query = "Write a quick sort algorithm in Python"
 text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-output = text_gen(f"### Instruction:\n{query}\n### Response:\n")
 print(output[0]['generated_text'])
 """
-### Instruction:
-write a quick sort algorithm in Python
-### Response:
-def quick_sort(arr):
-    if len(arr) <= 1:
-        return arr
-    else:
-        pivot = arr[len(arr) // 2]
-        left = [x for x in arr if x < pivot]
-        middle = [x for x in arr if x == pivot]
-        right = [x for x in arr if x > pivot]
-        return quick_sort(left) + middle + quick_sort(right)
-arr = [5,2,4,3,1]
-print(quick_sort(arr))
-"""
-[1, 2, 3, 4, 5]
 """
 ```
 ## Metrics
-[Detailed metrics](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_code_instruct_0.1)
 ```
-|  Tasks   |Version|Filter|n-shot|Metric|Value |   |Stderr|
-|----------|-------|------|-----:|------|-----:|---|-----:|
-|winogrande|Yaml   |none  |     0|acc   |0.6267|±  |0.0136|
-|hellaswag|Yaml   |none  |     0|acc     |0.4962|±  |0.0050|
-|         |       |none  |     0|acc_norm|0.6581|±  |0.0047|
-|arc_challenge|Yaml   |none  |     0|acc     |0.3481|±  |0.0139|
-|             |       |none  |     0|acc_norm|0.3712|±  |0.0141|
-|truthfulqa|N/A    |none  |     0|bleu_max   | 24.2580|±  |0.5985|
-|          |       |none  |     0|bleu_acc   |  0.2876|±  |0.0003|
-|          |       |none  |     0|bleu_diff  | -8.3685|±  |0.6065|
-|          |       |none  |     0|rouge1_max | 49.3907|±  |0.7350|
-|          |       |none  |     0|rouge1_acc |  0.2558|±  |0.0002|
-|          |       |none  |     0|rouge1_diff|-10.6617|±  |0.6450|
-|          |       |none  |     0|rouge2_max | 32.4189|±  |0.9587|
-|          |       |none  |     0|rouge2_acc |  0.2142|±  |0.0002|
-|          |       |none  |     0|rouge2_diff|-12.9903|±  |0.9539|
-|          |       |none  |     0|rougeL_max | 46.2337|±  |0.7493|
-|          |       |none  |     0|rougeL_acc |  0.2424|±  |0.0002|
-|          |       |none  |     0|rougeL_diff|-11.0285|±  |0.6576|
-|          |       |none  |     0|acc        |  0.3072|±  |0.0405|
 ```

 ---
+base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
 datasets:
 - mwitiderrick/AlpacaCode
 inference: true
 model_type: llama
 prompt_template: |
+  <s>[INST]
   {prompt}
+  [/INST]
 created_by: mwitiderrick
 tags:
 - transformers
         metrics:
           - name: hellaswag(0-Shot)
             type: hellaswag (0-Shot)
+            value: 0.
       - task:
           type: text-generation
         dataset:
         metrics:
           - name: winogrande(0-Shot)
             type: winogrande (0-Shot)
+            value: 0.
       - task:
           type: text-generation
         metrics:
           - name: arc_challenge(0-Shot)
             type: arc_challenge (0-Shot)
+            value: 0.
         source:
           name: open_llama_3b_instruct_v_0.2 model card
           url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
 ---
 # OpenLLaMA Code Instruct: An Open Reproduction of LLaMA
+This is an [OpenLlama model Code Instruct](https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1) that has been fine-tuned on 1 epoch of the
+[Glaive Assistsnt](https://huggingface.co/datasets/mwitiderrick/glaive-code-assistant) dataset.
 ## Prompt Template
 ```
+<s>[INST] {{ user_msg }} [/INST]
 ```
 ## Usage
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
+tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
+model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
 query = "Write a quick sort algorithm in Python"
 text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+output = text_gen(f<s>[INST]{query}[/INST]")
 print(output[0]['generated_text'])
 """
 """
 ```
 ## Metrics
 ```
 ```