Phind CodeLlama 34B v2

Phind's CodeLlama-derived coder at 34B. Older release; retained for historical / continuity value. Newer Qwen Coder lineage has surpassed it.

License: Llama 2 Community License·Released Sep 1, 2023·Context: 16,384 tokens

BLK · VERDICT

Our verdict

OP · Eruo Fredoline|UPDATED NOV 8, 2026

Not tested by usEditorial assessment from specifications and published reports.

unrated

Positioning

Phind CodeLlama 34B v2 is Phind's code-generation fine-tune on CodeLlama 34B — one of the strongest specialist coding models in the 30B weight class. It's built on Meta's CodeLlama base with Phind's proprietary code instruction dataset, targeting developers who need an on-prem code assistant that doesn't require frontier hardware. The v2 weights were uploaded in August 2023, and it was state-of-the-art for open-weight coding at its size. The operator-grade question in 2026: "does this older code specialist still hold up against Qwen 2.5 Coder 32B and DeepSeek Coder V2 236B?"

Strengths

Strong code generation for 2023/2024 era. Phind v2 was one of the first open-weight models to demonstrate competitive HumanEval scores. It can generate, explain, and debug code across many languages.
Fits a single 24-GB consumer card at Q4. Q4_K_M at ~20 GB — comfortable on RTX 4090, RTX 3090. Leaves headroom for KV cache at 8-16K context.
Mature tooling support. As a Llama-family model, it works with llama.cpp, Ollama, vLLM, and all standard inference engines.
Active during its peak. Phind v2 had strong community adoption in late 2023/early 2024. Known failure modes, edge cases, and workarounds are documented.

Limitations

Overtaken by newer code specialists. Qwen 2.5 Coder 32B is in the same weight class with newer training. DeepSeek Coder V2 236B is far larger.
Base model is CodeLlama 34B (Meta, 2023). The underlying Llama architecture and training data are from the Llama 2 era.
Knowledge stops no later than August 2023. The weights were uploaded on 28 August 2023, so language features, library APIs, and frameworks newer than that are absent.
Context window is limited. CodeLlama 34B base has a 16K native context window — tight for full-codebase analysis.
No longer actively maintained. Phind has moved on to newer models. v2 is in maintenance mode. No updates for new languages, frameworks, or patterns.
Tool-use and agent workflows are weak. Phind v2 predates the function-calling era. It generates code but doesn't reliably orchestrate multi-tool chains.
License: Llama 2 Community License. More restrictive than Llama 3's license. Verify terms for commercial deployment.

Real-world performance

vs Qwen 2.5 Coder 32B: Qwen 2.5 Coder 32B is the better pick: newer, with an Apache 2.0 license. No reason to pick Phind v2 over Qwen 2.5 Coder 32B for new deployments.
vs DeepSeek Coder V2 236B: DeepSeek Coder V2 is much larger and needs far more hardware. Phind v2 fits a single 24-GB card. The deployment envelope is different, but Qwen 2.5 Coder 32B fills that same 24-GB envelope better.
vs CodeGemma 7B: CodeGemma is much smaller and faster but meaningfully less capable. Phind v2 at 34B is in a different weight class. For pure speed on simple tasks, CodeGemma. For capability on complex coding, neither is current best-in-class.
vs Codestral Mamba 7B: Codestral Mamba is Mamba-architecture, fast, fits small cards. Phind v2 is larger, slower but more capable for complex coding tasks. Different deployment envelopes.

Should you run this locally?

Probably not for new deployments. In 2026, Qwen 2.5 Coder 32B fits the same hardware. The only reason to reach for Phind v2 is if you have an existing deployment with custom fine-tunes that would be costly to migrate.

Yes if you have an existing Phind v2 pipeline and it works. Migration cost is real. But plan the migration: Qwen 2.5 Coder 32B is the forward path.

No for any greenfield coding deployment. Pick Qwen 2.5 Coder 32B or DeepSeek Coder V2 236B.

How it compares

vs Qwen 2.5 Coder 32B: Superseded. Same weight class, newer model.
vs DeepSeek Coder V2 236B: Different deployment tiers. Phind v2 fits 24 GB; DeepSeek Coder V2 236B needs about 143 GB of weights at Q4_K_M.
vs CodeGemma 7B: Different weight classes. Phind v2 at 34B is more capable and slower. CodeGemma 7B is for simple tasks on small cards.
vs Llama 3.3 70B: Llama 3.3 70B is general-purpose with solid coding. Phind v2 is code-specialized. For pure coding, Phind v2 was historically better; now both are outclassed by newer specialists.

Run this yourself

Single-card workstation: RTX 4090 (24 GB) at Q4_K_M with 16K context via Ollama or llama.cpp.
Vendor: huggingface.co/Phind/Phind-CodeLlama-34B-v2

Overview

Phind's CodeLlama-derived coder at 34B. Older release; retained for historical / continuity value. Newer Qwen Coder lineage has surpassed it.

How to run it

Phind-CodeLlama-34B-v2 is Phind's code-specialized fine-tune of CodeLlama 34B. Run at Q4_K_M via Ollama (ollama pull phind-codellama:34b-v2-q4_K_M) or llama.cpp with -ngl 999 -fa -c 4096. Q4_K_M file size ~20 GB on disk. Minimum VRAM for full GPU offload at Q4_K_M: 24 GB. Q4_K_M weights are 20.2 GB, so an RTX 4080 (16GB) needs partial CPU offload. RTX 4090 24GB: Q4_K_M at 8-16K context. Recommended: RTX 4090 24GB at Q4_K_M. CodeLlama architecture — well-supported. Phind's fine-tune focuses on code generation, using 1.5B tokens of instruction-answer programming data. Strong on: code generation, debugging, code explanation, technical Q&A. Less strong on: general chat, creative writing, non-technical tasks. Phind-v2 improves on v1 with better instruction-following and multi-language code support. Context: 16K advertised (CodeLlama base). Practical at Q4 on 24 GB is 8-16K. For newer code models, consider DeepSeek Coder V2 236B or Qwen 2.5 Coder 32B.

Hardware guidance

Minimum: RTX 3060 12GB with partial CPU offload only, since Q3_K_M weights are 16.3 GB. Recommended: RTX 4090 24GB at Q4_K_M (16K context). Optimal: RTX 4090 24GB at Q4_K_M. VRAM math: 34B dense, Q4_K_M weights are 20.2 GB. KV cache at 16K: ~3.2 GB (48 layers x 2 x 8 KV heads x 128 head dim x 2 bytes x 16,384 tokens). Total: ~23.4 GB at 16K, tight on a 24 GB card. RTX 4090 24GB: Q4 + 8K = ~21.8 GB, fits on-GPU. RTX 3090 24GB: same. RTX 4080 16GB: Q4 needs partial CPU offload, because the weights exceed 16 GB. RTX 5090 32GB: Q4 at 16K, the model's context limit. MacBook Pro M4 Pro 24GB+: Q4. Cloud: A10 24GB at Q4_K_M. Code generation typically doesn't need 16K+ context — 4-8K is sufficient for most coding tasks. AWQ-INT4 drops weights to ~17 GB.

What breaks first

Code quality at Q3. Code generation is precision-sensitive. Q3 quantization introduces subtle bugs — variable name errors, syntax mistakes, incorrect API calls. Use Q4_K_M minimum for code. 2. No fill-in-the-middle (FIM). Meta lists infilling only for the 7B and 13B Code Llama models, so do not plan on FIM completion with this 34B fine-tune. 3. Prompt format. Phind v2 is instruction-tuned on the Alpaca/Vicuna format (### System Prompt, ### User Message, ### Assistant). Using standard Llama chat template breaks code generation formatting. 4. Language-specific quality variance. Phind-CodeLlama's code quality varies by language. Python and TypeScript are strongest; less common languages may have more errors. Test your target language.

Runtime recommendation

Ollama for quick-start. llama.cpp for local use. vLLM for serving. For IDE integration: Continue.dev or TabbyAPI. CodeLlama architecture is well-supported.

Common beginner mistakes

Mistake: Using Phind-CodeLlama for general chat. Fix: It's code-specialized. General knowledge and conversational ability are degraded vs same-sized general models. Use for code tasks only. Mistake: Expecting FIM completion. Fix: Meta lists infilling only for the 7B and 13B Code Llama models. Send instructions in the ### System Prompt / ### User Message / ### Assistant format instead. Mistake: Using Q3 for production code generation. Fix: Q3 introduces subtle bugs. Test your code outputs at Q3 vs Q4 — you'll likely find more syntax errors and hallucinated APIs. Use Q4_K_M minimum. Mistake: Expecting Phind-v2 to know APIs released after its training cutoff. Fix: CodeLlama's knowledge is frozen. Use RAG with current documentation for recent API/language features.