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CodeLlama 70b Python hf

Code Llama · Version meta-llama/CodeLlama-70b-Python-hf

AvailableChecked Oct 23, 2026
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Publisher-sourced inventory. Specifications come from official documentation and model cards. “Not documented” means we have no verified value. This inventory is separate from our editorial reviews.

Coverage & sources ↗

Current catalog answers

What to know about CodeLlama 70b Python hf

AI-compiled from linked model documentation. This is a catalog reference, not an independent performance review. Catalog checked October 23, 2026.

Which CodeLlama 70b Python hf does this page describe?

CodeLlama 70b Python hf is a model record from Meta in the Code Llama family. Source

The recorded model identifier or checkpoint is meta-llama/CodeLlama-70b-Python-hf. Use that exact identifier when checking documentation or a provider listing. Source

How can you access CodeLlama 70b Python hf?

The catalog documents Open weights access for CodeLlama 70b Python hf. Source

As of the catalog check, this version was listed as available. Source

Open weights means the model weights are distributed for use under their stated terms. It does not establish a hosted API, an open-source license, or a hardware requirement.

What can CodeLlama 70b Python hf receive and generate?

Documented inputs: text. Source

Documented outputs: text. Source

Input support and output generation are separate capabilities. Reading an image does not establish image generation; a text output listing does not establish speech output. File sizes, formats and media limits depend on the documented implementation.

What license is recorded for CodeLlama 70b Python hf?

The recorded license label is Custom commercial Code Llama license. Source

Read the linked license and associated use terms for this exact version before redistribution, commercial deployment or fine-tuning. A label alone does not establish every permission, restriction or obligation, and it does not determine the terms of a separate hosted service.

What is documented about the CodeLlama 70b Python hf checkpoint?

This evidence refers to meta-llama/CodeLlama-70b-Python-hf at recorded revision b916062ff81cbd5d51efe623b1b3f6ee3b07b7e6, checked October 23, 2026. Repository metadata

Safetensors metadata records 68,976,910,336 stored parameters. This is a stored-weight count, not necessarily the number activated per token, a download size or a GPU-memory requirement. Repository metadata

Recorded architecture class: LlamaForCausalLM. Repository metadata

Repository task tag: text-generation. A task tag identifies a distribution category; it does not measure performance on that task. Repository metadata

Listed library: transformers. Check that library's version and model support before loading the weights. Repository metadata

Repository language labels: code. These labels are not independent evaluations of language quality. Repository metadata

Repository access gate: manual. This records the repository's gate setting when checked; licensing and use terms still apply. Repository metadata

Read the retrieved model card for setup and qualifications. Model card

Keep the exact version in view

Other Code Llama models from Meta.

These are separate records in the same publisher family. A shared family name does not establish equal capabilities or interchangeable licenses.

CodeLlama 13b hf

Open weights · 100,000 context tokens

CodeLlama 34b hf

Open weights · 100,000 context tokens

The documented record

Specifications, with their sources.

A missing value remains unknown. Input limits, output limits and the total context window are separate measurements.

Publisher
MetaSource ↗
Family
Code LlamaSource ↗
Version
meta-llama/CodeLlama-70b-Python-hfSource ↗
Availability
AvailableSource ↗
Access
Open weightsSource ↗
Inputs
TextSource ↗
Outputs
TextSource ↗
Context window
Not documented
Maximum input
Not documented
Maximum output
Not documented
Knowledge cutoff
Not documented
Training-data cutoff
Not documented
Training completed
Not documented
Release date
Not documented
License
Custom commercial Code Llama licenseSource ↗
Input price / 1M tokens
Not documented
Output price / 1M tokens
Not documented

Read this with the numbers.

Vendor describes up to 100K inference context for most variants and 16K training sequences. Python 70B and Instruct 70B excluded from that stated inference claim. Publisher repository evidence (2026-10-23): Publisher task label: text-generation. Published architecture metadata: LlamaForCausalLM. Repository library label: transformers. Model-card language labels: code; these labels are not evaluated proficiency scores. Published safetensors metadata counts 68,976,910,336 stored parameters; this is not an active-parameter count for mixture-of-experts models. Repository access metadata is manual; access gating does not establish license permissions.

Model identifiers and aliases 0

No additional identifier has been verified.

Follow the evidence

Official sources.

  1. MODEL_CARD.md ↗github.com
  2. CodeLlama 70b Python hf official reference ↗huggingface.co
  3. CodeLlama 70b Python hf: official publisher repository metadata ↗huggingface.co
  4. CodeLlama 70b Python hf: publisher model card at revision b916062ff81c ↗huggingface.co

From the Headless Thrones newsroom

Editorial about CodeLlama 70b Python hf

No published articles are linked to this exact model yet. New coverage will appear here as it is published.

Your starting point

CodeLlama 70b Python hf

Meta · Open weights

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Inputs
TextSource ↗
Outputs
TextSource ↗
Context window
Not documented
Knowledge cutoff
Not documented

A place to begin comparing

1,313 models to consider.

Matches must share an access route, support every documented input type of your starting model, and return text. When both publish a context limit in the same field, the candidate must fall between half and twice that limit. Unknown or differently defined limits remain as broader matches, after verified near-context matches. Within each group, recent documented releases appear first. These criteria do not establish equal quality. The full current inventory is considered.

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Text input; text output · Shared access: Open weights · Capacity match not established

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Text input; text output · Shared access: Open weights · Capacity match not established

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Text input; text output · Shared access: Open weights · Capacity match not established

Microsoft

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Text input; text output · Shared access: Open weights · Capacity match not established

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Text input; text output · Shared access: Open weights · Capacity match not established

A record you can follow

The source belongs beside the fact.

Official sources

Model records cite the developer’s documentation, release notes and model cards. A listing alone is not a tested service or a recommendation.

Dates mean different things

Knowledge cutoff, training-data cutoff, training completion and release date are separate fields. A model’s name or repository upload date does not establish any of them.

Limits need context

Some publishers use rounded K/M token figures. Deployment, reasoning settings and regional access can change usable limits. Read the source before choosing.

Coverage and last check

Independent editorial model library assembled from primary vendor documentation and official publisher model cards. This is a dated, auditable inventory of the sources reviewed, not a claim to every model worldwide. First-party language-model inventory for OpenAI, Anthropic, Google Gemini/Gemma, xAI, Meta and Reflection. Includes vendor-listed legacy/retired identities with explicit statuses; official pretrained and instruction-tuned checkpoints remain distinct. Excludes embeddings, moderation/guard models, image/audio/video-only generation, community fine-tunes, speculative draft helpers, format/runtime/quantization duplicates, and PaliGemma dataset-specific benchmark fine-tunes. Availability means documented vendor API or downloadable official weights, not guaranteed account eligibility. Unknown specifications and dates remain null. Rounded K/M limits preserve vendor granularity; exact integers are used only where documented. No OpenRouter facts are used. Primary-source inventory of language-capable models from Alibaba/Qwen, DeepSeek, Moonshot, MiniMax, Z.ai, ByteDance Seed, Tencent, Baidu, Xiaomi, StepFun, 01.AI, Shanghai AI Laboratory, Baichuan and OpenBMB. Includes full reachable official public weight inventories plus documented hosted API models and explicit retirements. Embeddings, reward/classifier/router/tokenizer/draft-head components, standalone OCR/document converters and image/video/audio-only generators are excluded; multimodal language models retained. Official packaging variants are folded into aliases where a canonical listing was identifiable; remaining research/base/SFT variants retain exact identities. Not a guarantee every historical/private/regional model is available. Unknown specifications remain null; K/M API display quantities use decimal normalization where explicitly noted, rather than implying verified exact serving ceilings. Model-name dates and repository creation timestamps are never used as release/training dates. Open weights does not mean unrestricted or OSI-open-source licensing. Some model-card bodies were unreachable; their public inventory metadata is retained and disclosed. Official Mistral, Cohere, AI21, NVIDIA Nemotron, Microsoft Phi/MAI, Amazon Nova, IBM Granite, AllenAI OLMo/Molmo, TII Falcon, Nous Research, Arcee and Essential AI model inventory checked 2026-10-23. Includes base, instruction, reasoning, coding and text-producing multimodal models, including legacy API versions. Excludes embeddings/rerankers/reward and safety classifiers, image/audio/video-only generation, robotics-only models, duplicate quantization/runtime packages, adapters, intermediate checkpoint experiments, and third-party mirrors. Arcee coverage focuses on its named product families rather than exploratory merge repositories. Repository listing means listed, not independently verified API availability. K/M figures are nominal decimal normalizations when the source supplies shorthand; deployment-specific exact token limits may differ. Unverified values remain null. This is a source-backed inventory, not a complete claim-level independent editorial review. Supplemental official Liquid AI, Reka and Inception generative language catalog. Excludes media-only generators and decision/classification-only models. No assumed training dates.

Catalog edition updated . Source links are provided on each model record.

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