Using Phind CodeLlama 34B V2 on Together AI
Implementation guide · Phind CodeLlama · Phind
Quick Start
- 1
- 2Use the Together AI SDK or REST API to call
phind-codellama-34b-v2— see the documentation for request format. - 3
Code Examples
pip install togetherTOGETHER_API_KEYphind-codellama-34b-v2Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="phind-codellama-34b-v2",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)About Together AI
Platform for running open-source and proprietary LLMs
Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Pricing on Together AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.80 |
| Output tokens | $0.80 |
Capabilities
About Phind CodeLlama 34B V2
Phind CodeLlama 34B v2 is a large language model designed specifically for code generation tasks, built on the CodeLlama architecture. It generates high-quality code in multiple programming languages such as Python, C/C++, TypeScript, and Java, and is instruction-tuned for enhanced usability. The model demonstrates strong benchmark performance, achieving a 73.8% pass@1 score on the HumanEval benchmark. It has been fine-tuned on a proprietary dataset of 1.5 billion tokens, focusing on instruction-answer pairs. Additionally, it showcases multi-lingual capabilities beyond programming languages and offers various quantized versions like GPTQ and GGUF for optimized performance and reduced memory usage. Despite its impressive features, thorough testing is advised prior to real-world deployment, as its testing has been limited so far 1 2 7.