Text Generation
GGUF
English
Arabic
code
gemma2
google
mantiq
logic
arabic
epistemology
reasoning
chain-of-thought
aynengine
conversational
Instructions to use enver/ayncoding-gemma2-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use enver/ayncoding-gemma2-2b with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf enver/ayncoding-gemma2-2b # Run inference directly in the terminal: llama cli -hf enver/ayncoding-gemma2-2b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf enver/ayncoding-gemma2-2b # Run inference directly in the terminal: llama cli -hf enver/ayncoding-gemma2-2b
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf enver/ayncoding-gemma2-2b # Run inference directly in the terminal: ./llama-cli -hf enver/ayncoding-gemma2-2b
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf enver/ayncoding-gemma2-2b # Run inference directly in the terminal: ./build/bin/llama-cli -hf enver/ayncoding-gemma2-2b
Use Docker
docker model run hf.co/enver/ayncoding-gemma2-2b
- LM Studio
- Jan
- vLLM
How to use enver/ayncoding-gemma2-2b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "enver/ayncoding-gemma2-2b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "enver/ayncoding-gemma2-2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/enver/ayncoding-gemma2-2b
- Ollama
How to use enver/ayncoding-gemma2-2b with Ollama:
ollama run hf.co/enver/ayncoding-gemma2-2b
- Unsloth Desktop
- Docker Model Runner
How to use enver/ayncoding-gemma2-2b with Docker Model Runner:
docker model run hf.co/enver/ayncoding-gemma2-2b
- Lemonade
How to use enver/ayncoding-gemma2-2b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull enver/ayncoding-gemma2-2b
Run and chat with the model
lemonade run user.ayncoding-gemma2-2b-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 20,370 Bytes
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"""
code_lexicon_mapper.py
AynEngine AI Coding Edition (v2.0): Epistemic Classical Lexicon Bridge
Maps modern software engineering concepts and programming invariants to the 5 Classical Arabic Lexicographical & Grammatical Pillars:
1. Al-Mufradāt fī Gharīb al-Qurʾān (al-Rāghib al-Iṣfahānī) -> Ontological Domain Modeling & Teleology
2. Asās al-Balāghah (al-Zamakhsharī) -> Idiomatic Eloquence & Abstraction Integrity (Ḥaqīqah vs Majāz)
3. Lisān al-ʿArab (Ibn Manẓūr) -> Exhaustive State-Space, Edge-Cases, & Error Taxonomy
4. Kitāb al-ʿAyn (al-Farāhīdī) -> Atomic Primitive Decomposition & State Combinatorics
5. Al-Kitāb (Sībawayh) -> Syntactic Governance (ʿĀmil/Maʿmūl), AST Hierarchy & Strict Typing
"""
import re
from typing import Dict, List, Any, Optional
# Software Engineering Dimension -> Classical Roots & Lexical Conceptual Anchors
CONCEPT_ROOT_TAXONOMY = {
"concurrency": {
"roots": ["جمع", "زمن", "سوق", "حجز", "فوج", "جري"],
"pillar_focus": "Kitāb al-ʿAyn & Sībawayh",
"description": "Multi-agent coordination, event loops, mutexes, and non-blocking scheduling"
},
"immutability": {
"roots": ["ثبت", "حفظ", "بقي", "صلب", "جمد", "عصم"],
"pillar_focus": "Al-Mufradāt & Asās al-Balāghah",
"description": "State permanence, pure functions, absence of side-effects, and persistent state structures"
},
"types": {
"roots": ["ميز", "حدّ", "صنف", "حكم", "فصل", "نعت"],
"pillar_focus": "Al-Kitāb (Sībawayh) & Al-Mufradāt",
"description": "Algebraic domain types, structural invariants, type guards, and compile-time correctness"
},
"error_handling": {
"roots": ["درء", "عطب", "كشف", "رجع", "سلم", "عذر"],
"pillar_focus": "Lisān al-ʿArab",
"description": "Exhaustive edge-case matching, error taxonomy, backpressure, and fault-tolerance"
},
"abstraction": {
"roots": ["جوز", "حقق", "لبس", "صفا", "رمز", "ستر"],
"pillar_focus": "Asās al-Balāghah",
"description": "Metaphor vs reality (Majāz vs Ḥaqīqah), zero leaky abstractions, and code minimalism"
},
"decomposition": {
"roots": ["أصل", "فصل", "فرع", "بسط", "جزء", "قسم"],
"pillar_focus": "Kitāb al-ʿAyn",
"description": "Orthogonal primitive decomposition, single-responsibility, and modular cohesion"
},
"governance": {
"roots": ["عمل", "حكم", "قود", "سلط", "ملك", "نظم"],
"pillar_focus": "Al-Kitāb (Sībawayh)",
"description": "Explicit caller-callee governance (ʿĀmil wa Maʿmūl), dependency inversion, and pipeline flow"
},
"teleology": {
"roots": ["قصد", "غيا", "حقق", "وضع", "عمد", "نهج"],
"pillar_focus": "Al-Mufradāt",
"description": "Domain purpose (Ghāyah), self-evident naming, and semantic contracts"
},
# v2 Specialized Domain Expansions:
"networking_p2p": {
"roots": ["وصل", "نقل", "قطع", "فرق", "حبل", "ربط", "سلك"],
"pillar_focus": "Asās al-Balāghah & Lisān al-ʿArab",
"description": "Peer-to-peer topologies, bilateral sockets, ICE candidate exchange, and tunnel isolation"
},
"media_audio": {
"roots": ["صوت", "سمع", "نغم", "رجع", "صفو", "صخب"],
"pillar_focus": "Kitāb al-ʿAyn & Asās al-Balāghah",
"description": "Acoustic streams, PCM audio buffers, WebRTC track management, and echo cancellation"
},
"signaling_state": {
"roots": ["لوح", "علن", "بشر", "ندب", "وفد", "خطر"],
"pillar_focus": "Al-Kitāb (Sībawayh) & Al-Mufradāt",
"description": "SDP offer/answer handshakes, presence signals, state machines, and lifecycle transitions"
},
"cryptography": {
"roots": ["سرر", "وثق", "بدل", "قفل", "ختم", "حرز"],
"pillar_focus": "Al-Mufradāt & Kitāb al-ʿAyn",
"description": "End-to-end encryption, cryptographic ratchets, key exchange, and tamper-proof authentication"
},
"backpressure_queue": {
"roots": ["طبر", "حبس", "فرغ", "دفق", "كيل", "وسع"],
"pillar_focus": "Lisān al-ʿArab & Kitāb al-ʿAyn",
"description": "Bounded queues, backpressure propagation, buffer overflow prevention, and graceful draining"
},
"resilience": {
"roots": ["صمد", "درء", "عصم", "صلب", "نجا", "جبر"],
"pillar_focus": "Lisān al-ʿArab & Sībawayh",
"description": "Fault tolerance, circuit breaking, automatic reconnection, and self-healing systems"
}
}
KEYWORD_TO_DIMENSIONS = {
# Concurrency / Async / Threads
"async": ["concurrency", "governance"],
"await": ["concurrency", "governance"],
"thread": ["concurrency"],
"mutex": ["concurrency", "error_handling"],
"lock": ["concurrency", "error_handling"],
"channel": ["concurrency", "governance"],
"queue": ["concurrency", "backpressure_queue"],
"worker": ["concurrency", "governance"],
"pool": ["concurrency", "governance"],
"stream": ["concurrency", "media_audio"],
"parallel": ["concurrency"],
# Types / Contracts
"type": ["types", "governance"],
"class": ["types", "teleology"],
"interface": ["types", "abstraction"],
"struct": ["types", "teleology"],
"enum": ["types", "decomposition"],
"generic": ["types", "abstraction"],
"contract": ["types", "teleology"],
"invariant": ["types", "immutability"],
"schema": ["types", "teleology"],
# Immutability / State
"immutable": ["immutability"],
"const": ["immutability"],
"pure": ["immutability", "teleology"],
"state": ["immutability", "signaling_state"],
"cache": ["immutability", "concurrency"],
"store": ["immutability", "teleology"],
# Errors / Safety / Resilience
"error": ["error_handling", "resilience"],
"exception": ["error_handling"],
"retry": ["error_handling", "resilience"],
"fallback": ["error_handling", "resilience"],
"timeout": ["error_handling", "resilience"],
"circuit": ["error_handling", "resilience"],
"catch": ["error_handling"],
"panic": ["error_handling"],
"reconnect": ["resilience", "networking_p2p"],
# Networking / P2P / WebRTC
"p2p": ["networking_p2p", "signaling_state"],
"webrtc": ["networking_p2p", "media_audio"],
"peer": ["networking_p2p", "governance"],
"socket": ["networking_p2p", "governance"],
"mesh": ["networking_p2p", "decomposition"],
"ice": ["networking_p2p", "signaling_state"],
"sdp": ["signaling_state", "networking_p2p"],
"handshake": ["signaling_state", "networking_p2p"],
"signaling": ["signaling_state", "governance"],
"tunnel": ["networking_p2p", "resilience"],
"connection": ["networking_p2p", "signaling_state"],
# Audio / Video / Media
"audio": ["media_audio", "networking_p2p"],
"video": ["media_audio", "networking_p2p"],
"pcm": ["media_audio", "decomposition"],
"track": ["media_audio", "governance"],
"codec": ["media_audio", "decomposition"],
"sound": ["media_audio"],
"microphone": ["media_audio", "error_handling"],
"echo": ["media_audio", "resilience"],
# Cryptography / Security
"crypto": ["cryptography"],
"encrypt": ["cryptography"],
"decrypt": ["cryptography"],
"key": ["cryptography", "types"],
"e2ee": ["cryptography", "networking_p2p"],
"signature": ["cryptography", "teleology"],
"ratchet": ["cryptography", "signaling_state"],
# Backpressure / Buffers
"buffer": ["backpressure_queue", "immutability"],
"backpressure": ["backpressure_queue", "error_handling"],
"drain": ["backpressure_queue", "concurrency"],
"flush": ["backpressure_queue", "concurrency"],
"overflow": ["backpressure_queue", "error_handling"],
# Abstraction / Architecture
"architecture": ["abstraction", "governance", "decomposition"],
"pattern": ["abstraction", "decomposition"],
"service": ["teleology", "governance"],
"repository": ["abstraction", "teleology"],
"controller": ["governance", "teleology"],
"middleware": ["governance", "abstraction"],
"factory": ["abstraction", "decomposition"],
"refactor": ["abstraction", "decomposition", "governance"]
}
class AynCodeLexiconMapper:
"""
Connects programming requests and source code to the 5 Classical Arabic Lexicons:
1. Al-Mufradāt (al-Rāghib) -> Ontological Domain Teleology
2. Asās al-Balāghah (al-Zamakhsharī) -> Ḥaqīqah vs Majāz Abstraction Integrity
3. Lisān al-ʿArab (Ibn Manẓūr) -> Exhaustive State-Space & Error Taxonomy
4. Kitāb al-ʿAyn (al-Farāhīdī) -> Atomic Primitives & Phonetic/Structural Permutations
5. Al-Kitāb (Sībawayh) -> Syntactic Governance & Caller-Callee Hierarchy
"""
def __init__(self, **lexicon_mappings: Any):
self.lisan_dict = lexicon_mappings.get("lisan_dict") or {}
self.ayn_dict = lexicon_mappings.get("ayn_dict") or {}
self.raghib_dict = lexicon_mappings.get("raghib_dict") or {}
self.zamakhshari_dict = lexicon_mappings.get("zamakhshari_dict") or {}
self.sibawayh_rules = lexicon_mappings.get("sibawayh_rules") or {}
def extract_relevant_dimensions(self, text: str) -> List[str]:
"""Analyzes text/prompt/code and determines active software engineering dimensions."""
tokens = re.findall(r'[a-zA-Z_]+', text.lower())
dimension_counts: Dict[str, int] = {}
for token in tokens:
target_dims = KEYWORD_TO_DIMENSIONS.get(token, [])
for dim in target_dims:
dimension_counts[dim] = dimension_counts.get(dim, 0) + 1
# Always ensure core structural dimensions are active
default_dims = ["teleology", "abstraction", "governance"]
for d in default_dims:
dimension_counts[d] = dimension_counts.get(d, 0) + 1
sorted_dims = sorted(dimension_counts.items(), key=lambda x: x[1], reverse=True)
return [d[0] for d in sorted_dims[:5]]
def extract_relevant_roots(self, text: str) -> List[str]:
"""Extracts candidate classical roots corresponding to the programming context."""
dims = self.extract_relevant_dimensions(text)
roots = []
for d in dims:
if d in CONCEPT_ROOT_TAXONOMY:
roots.extend(CONCEPT_ROOT_TAXONOMY[d]["roots"])
# Deduplicate while preserving priority order
seen = set()
unique_roots = []
for r in roots:
if r not in seen:
seen.add(r)
unique_roots.append(r)
return unique_roots[:10]
def _find_ayn_entry(self, root: str) -> Optional[str]:
"""Looks up a root in Kitāb al-ʿAyn by exact or letter-spaced form."""
if not self.ayn_dict:
return None
# 1. Exact match
if root in self.ayn_dict:
return str(self.ayn_dict[root])
# 2. Letter-spaced match ('ج م ع' or 'ج م')
spaced = " ".join(list(root))
if spaced in self.ayn_dict:
return str(self.ayn_dict[spaced])
# 3. Two-letter root base match
if len(root) >= 2:
bi_spaced = f"{root[0]} {root[1]}"
if bi_spaced in self.ayn_dict:
return str(self.ayn_dict[bi_spaced])
return None
def _find_sibawayh_rule(self, dims: List[str]) -> Optional[str]:
"""Finds the most thematically relevant Sībawayh grammatical rule for the active dimensions."""
if not self.sibawayh_rules:
return None
keywords_map = {
"governance": ["عمل", "عامل", "معمول", "يرتفع"],
"types": ["اسم", "صفة", "نعت", "معرفة"],
"concurrency": ["بين", "جزأين", "حال", "تقديم"],
"signaling_state": ["إخبار", "ابتداء", "ظرف", "خبر"],
"error_handling": ["حذف", "قبح", "فصل", "منع"],
"resilience": ["لا", "توكيد", "بدل"]
}
target_words = []
for d in dims:
if d in keywords_map:
target_words.extend(keywords_map[d])
best_rule = None
best_score = -1
for title, content in self.sibawayh_rules.items():
combined = f"{title} {content}"
score = sum(1 for w in target_words if w in combined)
if score > best_score:
best_score = score
best_rule = f"{title} — {content}"
return best_rule or list(self.sibawayh_rules.values())[0]
def _clean_exemplar(self, text: str, max_len: int = 240) -> str:
"""Trims text cleanly at sentence/clause boundary rather than cutting mid-word."""
if not text:
return ""
clean = " ".join(text.replace('\n', ' ').split())
if len(clean) <= max_len:
return clean
# Find nearest natural boundary before max_len
cut = clean[:max_len]
delimiters = ['.', '!', '؟', '|', '،', ':', ';', '—', ' ']
best_pos = -1
for d in delimiters:
pos = cut.rfind(d)
if pos > best_pos and pos >= int(max_len * 0.6):
best_pos = pos
if best_pos > 0:
return clean[:best_pos].strip()
return cut.strip() + "..."
def build_epistemic_coding_context(self, prompt: str, language: str = "python") -> str:
"""
Builds the 5-Pillar Classical RAG context to ground code synthesis or review.
Dynamic, high-fidelity, and strictly grounded across all 5 classical authorities.
"""
dims = self.extract_relevant_dimensions(prompt)
roots = self.extract_relevant_roots(prompt)
header_lines = [
"🏛️ AYNENGINE AI (v2.0): 5-PILLAR CLASSICAL EPISTEMIC CODING APPARATUS",
f"Target Architecture / Language: {language.upper()}",
f"Active Conceptual Dimensions: {', '.join(dims).title()}",
""
]
section_lines = []
section_lines.extend(header_lines)
section_lines.extend(self._render_raghib_section(roots))
section_lines.extend(self._render_zamakhshari_section(roots))
section_lines.extend(self._render_lisan_section(roots))
section_lines.extend(self._render_farahidi_section(roots))
section_lines.extend(self._render_sibawayh_section(dims))
return "\n".join(section_lines)
def _render_raghib_section(self, roots: List[str]) -> List[str]:
"""Renders Pillar 1: Al-Mufradāt teleology anchor."""
output_rows = [
"1️⃣ AL-MUFRADĀT (Al-Rāghib al-Iṣfahānī) — Teleology & Ontological Domain Modeling:",
" • Invariant: Every type, entity, and function must have an unambiguous Ghāyah (teleology).",
" • Rule: Eliminate amorphous, bloated types (no generic 'amorphous_entity', 'processor', or 'manager')."
]
found_count = 0
for root_item in roots[:4]:
raghib_record = self.raghib_dict.get(root_item)
if not raghib_record:
continue
clean_def = self._clean_exemplar(raghib_record.get("definition", ""), 220)
if clean_def:
output_rows.append(f" • Root [{root_item}]: \"{clean_def}\"")
found_count += 1
if found_count >= 2:
break
if found_count == 0:
output_rows.append(" • Classical Anchor: Maintain strict ontological distinction between essential domain identity and accidental runtime state.")
return output_rows
def _render_zamakhshari_section(self, roots: List[str]) -> List[str]:
"""Renders Pillar 2: Asās al-Balāghah eloquence anchor."""
output_rows = [
"\n2️⃣ ASĀS AL-BALĀGHAH (Al-Zamakhsharī) — Rhetorical Eloquence & Abstraction Integrity (Ḥaqīqah vs Majāz):",
" • Invariant: Delineate literal runtime reality (CPU, IO, sockets, allocations) from software metaphors (ORMs, wrappers, promises).",
" • Rule: Zero leaky abstractions (Majāz Mukhil). Eliminate stuttering boilerplate; write idiomatic, high-impact code."
]
found_count = 0
for root_item in roots[2:7]:
zamakhshari_record = self.zamakhshari_dict.get(root_item)
if not zamakhshari_record:
continue
lit_usage = self._clean_exemplar(zamakhshari_record.get("literal_usage", ""), 140)
maj_usage = self._clean_exemplar(zamakhshari_record.get("metaphorical_usage", ""), 140)
if lit_usage or maj_usage:
output_rows.append(f" • Root [{root_item}]: [Ḥaqīqah: {lit_usage}] [Majāz: {maj_usage}]")
found_count += 1
if found_count >= 2:
break
if found_count == 0:
output_rows.append(" • Classical Anchor: Maximum communicative power with minimal syntactic ceremony; zero abstraction leakage.")
return output_rows
def _render_lisan_section(self, roots: List[str]) -> List[str]:
"""Renders Pillar 3: Lisān al-ʿArab coverage anchor."""
output_rows = [
"\n3️⃣ LISĀN AL-ʿARAB (Ibn Manẓūr) — Exhaustive State-Space, Edge-Cases & Error Taxonomy:",
" • Invariant: Exhaustive morphological coverage. Zero unhandled match cases, unhandled rejections, or silent failures.",
" • Rule: Model every state of the lifecycle: Initializing -> Active -> Degraded -> Closed -> Failed."
]
found_count = 0
for root_item in roots[:5]:
lisan_record = self.lisan_dict.get(root_item)
if not lisan_record:
continue
clean_def = self._clean_exemplar(str(lisan_record), 220)
if clean_def:
output_rows.append(f" • Root [{root_item}]: \"{clean_def}\"")
found_count += 1
if found_count >= 2:
break
return output_rows
def _render_farahidi_section(self, roots: List[str]) -> List[str]:
"""Renders Pillar 4: Kitāb al-ʿAyn primitive decomposition anchor."""
output_rows = [
"\n4️⃣ KITĀB AL-ʿAYN (Al-Farāhīdī) — Atomic Primitive Decomposition & State Permutations:",
" • Invariant: Decompose complex logic into orthogonal, irreducible mathematical primitives.",
" • Rule: Combinatorial state safety — Make illegal states unrepresentable in the type system.",
" • Ensure foundational primitives are pure, stateless, and idempotent."
]
found_count = 0
for root_item in roots:
ayn_entry = self._find_ayn_entry(root_item)
if not ayn_entry:
continue
ayn_clean = self._clean_exemplar(ayn_entry, 200)
output_rows.append(f" • Root Primitive [{root_item}]: \"{ayn_clean}\"")
found_count += 1
if found_count >= 1:
break
return output_rows
def _render_sibawayh_section(self, active_dims: List[str]) -> List[str]:
"""Renders Pillar 5: Al-Kitāb of Sībawayh syntactic governance anchor."""
output_rows = [
"\n5️⃣ AL-KITĀB (Sībawayh) — Syntactic Governance (ʿĀmil/Maʿmūl) & AST Integrity:",
" • Invariant: Strict caller-callee hierarchy. The Governor (ʿĀmil) explicitly controls the Governed (Maʿmūl).",
" • Rule: Zero circular dependencies. Strict static typing, pure information flow, and unambiguous function signatures."
]
rule_match = self._find_sibawayh_rule(active_dims)
if rule_match:
rule_clean = self._clean_exemplar(rule_match, 220)
output_rows.append(f" • Syntactic Canon: \"{rule_clean}\"")
return output_rows
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