Instructions to use patched-codes/patched-coder-34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use patched-codes/patched-coder-34b with Transformers:

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

pipe = pipeline("text-generation", model="patched-codes/patched-coder-34b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("patched-codes/patched-coder-34b")
model = AutoModelForCausalLM.from_pretrained("patched-codes/patched-coder-34b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use patched-codes/patched-coder-34b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "patched-codes/patched-coder-34b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "patched-codes/patched-coder-34b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/patched-codes/patched-coder-34b

SGLang

How to use patched-codes/patched-coder-34b 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 "patched-codes/patched-coder-34b" \
    --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": "patched-codes/patched-coder-34b",
		"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 "patched-codes/patched-coder-34b" \
        --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": "patched-codes/patched-coder-34b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use patched-codes/patched-coder-34b with Docker Model Runner:
```
docker model run hf.co/patched-codes/patched-coder-34b
```

codelion commited on Oct 2, 2023

Commit

93590d0

1 Parent(s): 768fb2e

Update README.md

Browse files

Files changed (1) hide show

README.md +44 -35

README.md CHANGED Viewed

@@ -3,42 +3,42 @@ inference: false
 datasets:
 - bigcode/commitpackft
 model-index:
-  - name: patched-coder-34b
-    results:
-      - task:
-          type: text-generation
-        dataset:
-          type: openai_humaneval
-          name: HumanEval
-        metrics:
-          - name: pass@1
-            type: pass@1
-            value: 53.567
-            verified: false
-      - task:
-          type: text-generation
-        dataset:
-          type: bigcode/humanevalpack
-          name: HumanEvalFix Python
-        metrics:
-          - name: pass@1
-            type: pass@1
-            value: 41.341
-            verified: false
-      - task:
-          type: text-generation
-        dataset:
-          type: patched-codes/static-analysis-eval
-          name: Static Analysis Eval
-        metrics:
-          - name: pass@1
-            type: pass@1
-            value: 51.316
-            verified: false
 ---
 # Model Card for patched-coder-34b
 This is an instruction fine-tuned model focussed on the task of patching code. Patching may include fixing bugs, remediating security vulnerabilities,
 doing API migrations and other kinds of code matainence.
@@ -113,9 +113,18 @@ The following `bitsandbytes` quantization config was used during training:
 ## Evaluation
-We evaluate the model on `HumanEval` and `HumanEvalPack` benchmarks using
 [Code Generation LM Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness).
-We also evaluate the model for vulnerability remediation using the `Static Analysis Eval` benchmark available [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval).
 ### Results

 datasets:
 - bigcode/commitpackft
 model-index:
+- name: patched-coder-34b
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 53.567
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Python
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 41.341
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: patched-codes/static-analysis-eval
+      name: Static Analysis Eval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 51.316
+      verified: false
+license: llama2
 ---
 # Model Card for patched-coder-34b
 This is an instruction fine-tuned model focussed on the task of patching code. Patching may include fixing bugs, remediating security vulnerabilities,
 doing API migrations and other kinds of code matainence.
 ## Evaluation
+We evaluated the model on `HumanEval` (for code generation) and `HumanEvalFix Python` (for bug fixing) benchmarks using
 [Code Generation LM Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness).
+To evaluate the model for vulnerability remediation we used the `Static Analysis Eval` benchmark available [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval).
 ### Results
+| Model | HumanEval | HumanEval Fix Python| Static Analysis Eval |
+| ----- | ----------| ------------------- | -------------------- |
+| GPT-4 | 86.6 | 47 | 55.26 |
+| patched-coder-34b | 53.57 | 41.34 | 51.32 |
+| CodeLlama-34b-Python | 53.29 | 33.14 | 27.63 |
+Based on the results on these benchmarks, patched-coder-34b is the SOTA open code LLM. Other code LLMs (e.g. from WizardCoder and Phind) are trained on
+either unknown proprietary datasets or used OpenAI's APIs for training, thus making them unviable for commercial use.